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Journal articles on the topic 'White label social networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Alimadadi, Fatemeh, Ehsan Khadangi, and Alireza Bagheri. "Community detection in facebook activity networks and presenting a new multilayer label propagation algorithm for community detection." International Journal of Modern Physics B 33, no. 10 (April 20, 2019): 1950089. http://dx.doi.org/10.1142/s0217979219500899.

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The emergence of online social networks has revolutionized millions of web users’ behavior so that their interactions with each other produce huge amounts of data on different activities. Community detection, herein, is one of the most important tasks. The very recent trend is to detect meaningful communities based on users’ interactions or the activity network. However, in many of such studies, authors consider the basic network model while almost ignoring the model of the interactions in the multi-layer network. In this research, an experimental study is done to compare community detection in Facebook friendship network to that of activity network, considering different activities in Facebook OSN such as sharing. Then, a new community detection evaluation metric based on homophily is proposed. Eventually, a new method of community detection based on different activities in Facebook social network is presented. In this method, we generalized three familiar similarity methods, Jaccard, Common Neighbors and Adamic-Adar for multi-layered network model.

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Bayfield, Hannah, Laura Colebrooke, Hannah Pitt, Rhiannon Pugh, and Natalia Stutter. "Awesome women and bad feminists: the role of online social networks and peer support for feminist practice in academia." cultural geographies 27, no. 3 (December 16, 2019): 415–35. http://dx.doi.org/10.1177/1474474019890321.

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In her book, ‘Bad Feminist’, Roxane Gay claims this label shamelessly, embracing the contradictory aspects of enacting feminist practice while fundamentally being ‘flawed human[s]’. This article tells a story inspired by and enacting Roxane Gay’s approach in academia, written by five cis-gendered women geographers. It is the story of a proactive, everyday feminist initiative to survive as women in an academic precariat fuelled by globalised, neoliberalised higher education. We reflect on what it means to be (bad) feminists in that context, and how we respond as academics. We share experiences of an online space used to support one another through post-doctoral life, a simple message thread, which has established an important role in our development as academics and feminists. This article, written through online collaboration, mirrors and enacts processes fundamental to our online network, demonstrating the significance and potential of safe digital spaces for peer support. Excerpts from the chat reflect critically on struggles and solutions we have co-developed. Through this, we celebrate and validate a strategy we know that we and others like us find invaluable for our wellbeing and survival. Finally, we reflect on the inherent limitations of exclusive online networks as tools for feminist resistance.

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Parikh, Pulkit, Harika Abburi, Niyati Chhaya, Manish Gupta, and Vasudeva Varma. "Categorizing Sexism and Misogyny through Neural Approaches." ACM Transactions on the Web 15, no. 4 (June 11, 2021): 1–31. http://dx.doi.org/10.1145/3457189.

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Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policymakers in studying and thereby countering sexism. The existing work on sexism classification has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s). 1 We also consider the related task of misogyny classification. While sexism classification is performed on textual accounts describing sexism suffered or observed, misogyny classification is carried out on tweets perpetrating misogyny. We devise a novel neural framework for classifying sexism and misogyny that can combine text representations obtained using models such as Bidirectional Encoder Representations from Transformers with distributional and linguistic word embeddings using a flexible architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. To evaluate the versatility of our neural approach for tasks pertaining to sexism and misogyny, we experiment with adapting it for misogyny identification. For categorizing sexism, we investigate multiple loss functions and problem transformation techniques to address the multi-label problem formulation. We develop an ensemble approach using a proposed multi-label classification model with potentially overlapping subsets of the category set. Proposed methods outperform several deep-learning as well as traditional machine learning baselines for all three tasks.

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Kuzmin, Konstantin, Mingming Chen, and Boleslaw K. Szymanski. "Parallelizing SLPA for Scalable Overlapping Community Detection." Scientific Programming 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/461362.

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Communities in networks are groups of nodes whose connections to the nodes in a community are stronger than with the nodes in the rest of the network. Quite often nodes participate in multiple communities; that is, communities can overlap. In this paper, we first analyze what other researchers have done to utilize high performance computing to perform efficient community detection in social, biological, and other networks. We note that detection of overlapping communities is more computationally intensive than disjoint community detection, and the former presents new challenges that algorithm designers have to face. Moreover, the efficiency of many existing algorithms grows superlinearly with the network size making them unsuitable to process large datasets. We use the Speaker-Listener Label Propagation Algorithm (SLPA) as the basis for our parallel overlapping community detection implementation. SLPA provides near linear time overlapping community detection and is well suited for parallelization. We explore the benefits of a multithreaded programming paradigm and show that it yields a significant performance gain over sequential execution while preserving the high quality of community detection. The algorithm was tested on four real-world datasets with up to 5.5 million nodes and 170 million edges. In order to assess the quality of community detection, at least 4 different metrics were used for each of the datasets.

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Steinberger, Ralf, and Bruno Pouliquen. "Cross-lingual Named Entity Recognition." Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources 30, no. 1 (August 10, 2007): 135–62. http://dx.doi.org/10.1075/li.30.1.09ste.

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Named Entity Recognition and Classification (NERC) is a known and well-explored text analysis application that has been applied to various languages. We are presenting an automatic, highly multilingual news analysis system that fully integrates NERC for locations, persons and organisations with document clustering, multi-label categorisation, name attribute extraction, name variant merging and the calculation of social networks. The proposed application goes beyond the state-of-the-art by automatically merging the information found in news written in ten different languages, and by using the aggregated name information to automatically link related news documents across languages for all 45 language pair combinations. While state-of-the-art approaches for cross-lingual name variant merging and document similarity calculation require bilingual resources, the methods proposed here are mostly language-independent and require a minimal amount of monolingual language-specific effort. The development of resources for additional languages is therefore kept to a minimum and new languages can be plugged into the system effortlessly. The presented online news analysis application is fully functional and has, at the end of the year 2006, reached average usage statistics of 600,000 hits per day.

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Aydin, Kevin, MohammadHossein Bateni, and Vahab Mirrokni. "Distributed Balanced Partitioning via Linear Embedding †." Algorithms 12, no. 8 (August 10, 2019): 162. http://dx.doi.org/10.3390/a12080162.

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Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15–25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by ≈ 0.5 % , which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-shard queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the world graph into several “shards”. Live experiments demonstrate an ≈ 40 % drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another.

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Omar, Ahmed, Tarek M. Mahmoud, Tarek Abd-El-Hafeez, and Ahmed Mahfouz. "Multi-label Arabic text classification in Online Social Networks." Information Systems 100 (September 2021): 101785. http://dx.doi.org/10.1016/j.is.2021.101785.

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Nikfarjam, Azadeh, Julia D. Ransohoff, Alison Callahan, Vladimir Polony, and Nigam H. Shah. "Profiling off-label prescriptions in cancer treatment using social health networks." JAMIA Open 2, no. 3 (July 22, 2019): 301–5. http://dx.doi.org/10.1093/jamiaopen/ooz025.

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Abstract Objectives To investigate using patient posts in social media as a resource to profile off-label prescriptions of cancer drugs. Methods We analyzed patient posts from the Inspire health forums (www.inspire.com) and extracted mentions of cancer drugs from the 14 most active cancer-type specific support groups. To quantify drug-disease associations, we calculated information component scores from the frequency of posts in each cancer-specific group with mentions of a given drug. We evaluated the results against three sources: manual review, Wolters-Kluwer Medi-span, and Truven MarketScan insurance claims. Results We identified 279 frequently discussed and therefore highly associated drug-disease pairs from Inspire posts. Of these, 96 are FDA approved, 9 are known off-label uses, and 174 do not have records of known usage (potentially novel off-label uses). We achieved a mean average precision of 74.9% in identifying drug-disease pairs with a true indication association from patient posts and found consistent evidence in medical claims records. We achieved a recall of 69.2% in identifying known off-label drug uses (based on Wolters-Kluwer Medi-span) from patient posts.

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Chen, Naiyue, Yun Liu, Haiqiang Chen, and Junjun Cheng. "Detecting communities in social networks using label propagation with information entropy." Physica A: Statistical Mechanics and its Applications 471 (April 2017): 788–98. http://dx.doi.org/10.1016/j.physa.2016.12.047.

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Peters, Stéphane, Yann Jacob, Ludovic Denoyer, and Patrick Gallinari. "Iterative Multi-label Multi-relational Classification Algorithm for complex social networks." Social Network Analysis and Mining 2, no. 1 (June 28, 2011): 17–29. http://dx.doi.org/10.1007/s13278-011-0034-8.

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Wu, Zhi-Hao, You-Fang Lin, Steve Gregory, Huai-Yu Wan, and Sheng-Feng Tian. "Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks." Journal of Computer Science and Technology 27, no. 3 (January 2012): 468–79. http://dx.doi.org/10.1007/s11390-012-1236-x.

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Iñiguez, Gerardo, Tzipe Govezensky, Robin Dunbar, Kimmo Kaski, and Rafael A. Barrio. "Effects of deception in social networks." Proceedings of the Royal Society B: Biological Sciences 281, no. 1790 (September 7, 2014): 20141195. http://dx.doi.org/10.1098/rspb.2014.1195.

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Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies (‘white’ lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agent-based model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these group-level effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.

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Hosseini, Razieh, and Alireza Rezvanian. "AntLP: ant‐based label propagation algorithm for community detection in social networks." CAAI Transactions on Intelligence Technology 5, no. 1 (January 10, 2020): 34–41. http://dx.doi.org/10.1049/trit.2019.0040.

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Trinh, Thanh, Dingming Wu, Joshua Zhexue Huang, and Muhammad Azhar. "Activeness and Loyalty Analysis in Event-Based Social Networks." Entropy 22, no. 1 (January 18, 2020): 119. http://dx.doi.org/10.3390/e22010119.

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Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.

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Xia, Liegang, Xiongbo Zhang, Junxia Zhang, Haiping Yang, and Tingting Chen. "Building Extraction from Very-High-Resolution Remote Sensing Images Using Semi-Supervised Semantic Edge Detection." Remote Sensing 13, no. 11 (June 3, 2021): 2187. http://dx.doi.org/10.3390/rs13112187.

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The automated detection of buildings in remote sensing images enables understanding the distribution information of buildings, which is indispensable for many geographic and social applications, such as urban planning, change monitoring and population estimation. The performance of deep learning in images often depends on a large number of manually labeled samples, the production of which is time-consuming and expensive. Thus, this study focuses on reducing the number of labeled samples used and proposing a semi-supervised deep learning approach based on an edge detection network (SDLED), which is the first to introduce semi-supervised learning to the edge detection neural network for extracting building roof boundaries from high-resolution remote sensing images. This approach uses a small number of labeled samples and abundant unlabeled images for joint training. An expert-level semantic edge segmentation model is trained based on labeled samples, which guides unlabeled images to generate pseudo-labels automatically. The inaccurate label sets and manually labeled samples are used to update the semantic edge model together. Particularly, we modified the semantic segmentation network D-LinkNet to obtain high-quality pseudo-labels. Specifically, the main network architecture of D-LinkNet is retained while the multi-scale fusion is added in its second half to improve its performance on edge detection. The SDLED was tested on high-spatial-resolution remote sensing images taken from Google Earth. Results show that the SDLED performs better than the fully supervised method. Moreover, when the trained models were used to predict buildings in the neighboring counties, our approach was superior to the supervised way, with line IoU improvement of at least 6.47% and F1 score improvement of at least 7.49%.

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Bonar, Indiana, and Paula Sonja Karlsson. "Marketing Scottish social enterprises using a label?" Social Enterprise Journal 15, no. 3 (August 8, 2019): 339–57. http://dx.doi.org/10.1108/sej-08-2018-0056.

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Purpose Social enterprises are competitive businesses in the marketplace, yet insubstantial research has investigated how they market their businesses. This paper aims to investigate the impact a social enterprise label – “Buy the Good Stuff” – used in Edinburgh has had on consumer awareness and explore whether a possible national label could be used as a marketing tool by social enterprises in Scotland. Design/methodology/approach The study uses a mixed-methods approach, consisting of an online questionnaire with 100 participants and seven semi-structured interviews with representatives of social enterprises involved in the marketing campaign in Edinburgh and representatives of social enterprises who were not involved in the campaign. Findings Findings indicate that the label used in Edinburgh has had little impact on increasing consumer awareness of social enterprises. However, a national label has the potential to help social enterprises increase consumer awareness. Yet, successful implementation requires thorough design of the label and broad support for its promotion. Practical implications The paper offers insights into the implementation of a national label. Managers of social enterprises and social enterprise networks should consider the findings when adopting marketing activities. Originality/value Findings contribute to the sparse literature regarding marketing activities of social enterprises. The paper provides evidence that the broader social enterprise sector and its representatives in Scotland should re-evaluate their position on the introduction of a national label, given that one priority identified for the sector is to create and promote a social enterprise brand which the SE code is not focussed on.

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Zhang, Qishan, Qirong Qiu, Wenzhong Guo, Kun Guo, and Naixue Xiong. "A social community detection algorithm based on parallel grey label propagation." Computer Networks 107 (October 2016): 133–43. http://dx.doi.org/10.1016/j.comnet.2016.06.002.

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Leu, Jenq-Shiou, Jheng-Huei Chen, and Kuen-Han Li. "Hybrid Search Scheme for Social Networks Supported by Dynamic Weighted Distributed Label Clustering." IEEE Transactions on Computers 64, no. 9 (September 1, 2015): 2586–94. http://dx.doi.org/10.1109/tc.2014.2378254.

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Roghani, Hamid, Asgarali Bouyer, and Esmaeil Nourani. "PLDLS: A novel parallel label diffusion and label Selection-based community detection algorithm based on Spark in social networks." Expert Systems with Applications 183 (November 2021): 115377. http://dx.doi.org/10.1016/j.eswa.2021.115377.

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Gaudin, James M., and Katheryn B. Davis. "Social Networks of Black and White Rural Families: A Research Report." Journal of Marriage and the Family 47, no. 4 (November 1985): 1015. http://dx.doi.org/10.2307/352345.

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Youssef, A. "Sharing knowledge: the White Journal and the power of social networks." Ultrasound in Obstetrics & Gynecology 53, no. 2 (February 2019): 279–80. http://dx.doi.org/10.1002/uog.20200.

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Fuhse, Jan A. "Theorizing social networks: the relational sociology of and around Harrison White." International Review of Sociology 25, no. 1 (January 2, 2015): 15–44. http://dx.doi.org/10.1080/03906701.2014.997968.

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Sergeeva, Natalya. "What makes an “innovation champion”?" European Journal of Innovation Management 19, no. 1 (January 11, 2016): 72–89. http://dx.doi.org/10.1108/ejim-06-2014-0065.

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Purpose – The purpose of this paper is to explore how an “innovation champion” identity is formulated in the context of UK construction sector. A conceptual model of “innovation champion” themes is derived from the literature on social identity and then validated through empirical research. Design/methodology/approach – In total, 30 semi-structured interviews have been conducted with UK construction sector practitioners. The sample was composed active participants within the Constructing Excellence – the UK construction sector’s network-type organisation for driving innovation. Findings – Practitioners socially constructed “innovation champion” identities through narratives about “self” or others. While some practitioners saw themselves as “innovation champions”, many others recognised CEOs as such. “Innovation champions” are commonly recognised as key individuals who promote innovations across businesses. Practical implications – Socially constructed identities are seen important to strategic decisions and future actions. The potential contribution to practice is to help current and new generations of practitioners to learn about how to be or become “innovation champions”. Social implications – The concept of social identity presents opportunities to enrich our understanding of the “innovation champion” label can stimulate people’s reflections on who they are and what they do. Originality/value – The present paper has extended upon prior research and theory by exploring the ways practitioners perceive themselves or others as “innovation champions”. While this research has focused on the UK construction sector, the findings are potentially useful for other sectors where “innovation champion” labels are commonly used.

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Yuan, Weiwei, Jiali Pang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, and Mohammed Al-Dhelaan. "Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning." Complexity 2019 (February 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/4906903.

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Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model.

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Azaouzi, Mehdi, and Lotfi Ben Romdhane. "An evidential influence-based label propagation algorithm for distributed community detection in social networks." Procedia Computer Science 112 (2017): 407–16. http://dx.doi.org/10.1016/j.procs.2017.08.045.

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Hashemi, Mahdi, and Margeret Hall. "Multi-label classification and knowledge extraction from oncology-related content on online social networks." Artificial Intelligence Review 53, no. 8 (April 17, 2020): 5957–94. http://dx.doi.org/10.1007/s10462-020-09839-0.

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Zhao, Yuxin, Shenghong Li, and Feng Jin. "Identification of influential nodes in social networks with community structure based on label propagation." Neurocomputing 210 (October 2016): 34–44. http://dx.doi.org/10.1016/j.neucom.2015.11.125.

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Cui, Lin, Dechang Pi, and Caiyin Wang. "Topic Discovery Algorithm Based on Mutual Information and Label Clustering under Dynamic Social Networks." International Journal of Database Theory and Application 9, no. 5 (May 31, 2016): 169–80. http://dx.doi.org/10.14257/ijdta.2016.9.5.17.

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Zarei, Bagher, Mohammad Reza Meybodi, and Behrooz Masoumi. "Detecting community structure in signed and unsigned social networks by using weighted label propagation." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 10 (October 2020): 103118. http://dx.doi.org/10.1063/1.5144139.

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Mutersbaugh, Tad. "Fighting Standards with Standards: Harmonization, Rents, and Social Accountability in Certified Agrofood Networks." Environment and Planning A: Economy and Space 37, no. 11 (November 2005): 2033–51. http://dx.doi.org/10.1068/a37369.

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In this paper I explore the remaking of globalized standards through harmonization, and its impact upon certified-organic and fair-trade agrofood networks. I focus on certification standards and discuss four shifts associated with globalized standards (an increased importance of multilateral institutions, changes to standards language, displacement of network-specific standards, and a shift away from relational standards). It is then argued, with reference to value-chain rent theory, that the shift to globalized standards has transformed rent relations in ways that benefit certain actors (that is, retailers) and imperil the earnings of others. In brief, globalized standards increase the costs of standards compliance, the full burden of which falls upon immiserated producers, to the point at which farmers see little economic advantage to certified-organic and fair-trade production. I then examine social-accountability standards that seek to ‘fight standards with standards’ by championing the consolidation of strong labor and environmental protections under a single label. The study suggests that a single-label strategy can be successful, yet must struggle to overcome a Polanyian double bind, for, in order to build broad coalitions necessary to extend the reach of protective standards, the coalitions must include corporate interests that prefer weaker, contract-based standards.

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Means, Taneisha. "Review of African Americans in White Suburbia: Social Networks and Political Behavior." American Review of Politics 37, no. 1 (February 10, 2020): 103–5. http://dx.doi.org/10.15763/issn.2374-779x.2020.37.1.103-105.

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Gaither, Sarah E., Negin R. Toosi, Laura G. Babbitt, and Samuel R. Sommers. "Exposure to Biracial Faces Reduces Colorblindness." Personality and Social Psychology Bulletin 45, no. 1 (June 6, 2018): 54–66. http://dx.doi.org/10.1177/0146167218778012.

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Across six studies, we demonstrate that exposure to biracial individuals significantly reduces endorsement of colorblindness as a racial ideology among White individuals. Real-world exposure to biracial individuals predicts lower levels of colorblindness compared with White and Black exposure (Study 1). Brief manipulated exposure to images of biracial faces reduces colorblindness compared with exposure to White faces, Black faces, a set of diverse monoracial faces, or abstract images (Studies 2-5). In addition, these effects occur only when a biracial label is paired with the face rather than resulting from the novelty of the mixed-race faces themselves (Study 4). Finally, we show that the shift in White participants’ colorblindness attitudes is driven by social tuning, based on participants’ expectations that biracial individuals are lower in colorblindness than monoracial individuals (Studies 5-6). These studies suggest that the multiracial population’s increasing size and visibility has the potential to positively shift racial attitudes.

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Kou, Huaizhen, Fan Wang, Chao Lv, Zhaoan Dong, Wanli Huang, Hao Wang, and Yuwen Liu. "Trust-Based Missing Link Prediction in Signed Social Networks with Privacy Preservation." Wireless Communications and Mobile Computing 2020 (November 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/8849536.

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With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

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Jokar, Ehsan, and Mohammad Mosleh. "Community detection in social networks based on improved Label Propagation Algorithm and balanced link density." Physics Letters A 383, no. 8 (February 2019): 718–27. http://dx.doi.org/10.1016/j.physleta.2018.11.033.

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Sattari, Mohammad, and Kamran Zamanifar. "A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks." Journal of Computational Science 25 (March 2018): 122–33. http://dx.doi.org/10.1016/j.jocs.2018.01.004.

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Keerthi Gorripati, Satya, and Valli Kumari. "Distributed Community Detection based on Apache Spark using Multi Label Propagation for Digital Social Networks." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 79. http://dx.doi.org/10.14419/ijet.v7i4.5.20016.

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Organization, Government and Individual (OGI) have popularized the use of Digital Social Networks (DSN) that reduces the processing time of social-aware tasks. To accomplish a community-based communication, each social-aware task should identify its community group. The identified group uses a task to avail all the DSN benefits to their customers / citizens. As a result, the community-based detection algorithm has played a significant role in literature. However, the existing algorithms have had several challenging issues, such as performance and scalability. Thus, a distributed community detection algorithm is presented using Apache Spark’s Resilient Distributed Data Set (RDD) framework based on the Scala programming language. The Apache Spark framework provides an ideal solution that offers ease of coding, performance, interactive mode and disk Input-Output bottlenecks in Hadoop /Map Reduce. Besides, it presents a platform of distributed community detection that reduces the computational computation by applying transformations, aggregations and joins. The experimental results show that the proposed framework achieves high accuracy for both real-world and synthetic networks.

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Raju, E., Y. Ramadevi, and K. Sravanthi. "CILPA: a cohesion index based label propagation algorithm for unveiling communities in complex social networks." International Journal of Information Technology 10, no. 4 (April 24, 2018): 435–45. http://dx.doi.org/10.1007/s41870-018-0190-4.

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Sattari, Mohammad, and Kamran Zamanifar. "A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks." Data & Knowledge Engineering 113 (January 2018): 155–70. http://dx.doi.org/10.1016/j.datak.2017.12.003.

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Berahmand, Kamal, and Asgarali Bouyer. "LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1850062. http://dx.doi.org/10.1142/s0217979218500625.

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Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

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Johnson, Anthony M. "‘‘I Can Turn It on When I Need To’’: Pre-college Integration, Culture, and Peer Academic Engagement among Black and Latino/a Engineering Students." Sociology of Education 92, no. 1 (December 7, 2018): 1–20. http://dx.doi.org/10.1177/0038040718817064.

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Drawing on interviews with 38 black and Latino/a engineering students at a predominantly white, elite university, I use a cultural analytic framework to explicate the role of pre–college integration in the heterogeneous psychosocial and academic experiences of students of color on predominantly white campuses. I identify three cultural strategies students of color adopt to navigate the university’s ethnoracially segregated peer network landscape and more specifically, engage majority–white academic peer networks: integration, marginalized segregation, and social adaptation. Integrators, who hail from predominantly white high schools, engage majority–white academic networks with ease, do not experience ethnoracial marginalization, and form predominantly white networks in college. Marginalized segregators, who come from predominantly black, Latino/a, or mixed high schools, exhibit discomfort engaging majority–white academic networks, experience ethnoracial marginalization, and form predominantly same–race or co–ethnic networks in college. Finally, social adapters, who come from high schools with varying ethnoracial compositions, manage their experiences with ethnoracial marginalization to engage majority–white academic networks with ease, and the ethnoracial composition of their college networks varies. The findings extend previous scholarship on the experiences of black and Latino/a students on predominantly white campuses and uncover the cultural processes that contribute to the reproduction of inequality among students of color.

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Pedulla, David S., and Devah Pager. "Race and Networks in the Job Search Process." American Sociological Review 84, no. 6 (November 7, 2019): 983–1012. http://dx.doi.org/10.1177/0003122419883255.

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Racial disparities persist throughout the employment process, with African Americans experiencing significant barriers compared to whites. This article advances the understanding of racial labor market stratification by bringing new theoretical insights and original data to bear on the ways social networks shape racial disparities in employment opportunities. We develop and articulate two pathways through which networks may perpetuate racial inequality in the labor market: network access and network returns. In the first case, African American job seekers may receive fewer job leads through their social networks than white job seekers, limiting their access to employment opportunities. In the second case, black and white job seekers may utilize their social networks at similar rates, but their networks may differ in effectiveness. Our data, with detailed information about both job applications and job offers, provide the unique ability to adjudicate between these processes. We find evidence that black and white job seekers utilize their networks at similar rates, but network-based methods are less likely to lead to job offers for African Americans. We then theoretically develop and empirically test two mechanisms that may explain these differential returns: network placement and network mobilization. We conclude by discussing the implications of these findings for scholarship on racial stratification and social networks in the job search process.

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Schützeichel, Rainer. "Harrison C. White: Markets from networks: Socioeconomic models of production." KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie 56, no. 4 (December 2004): 760–62. http://dx.doi.org/10.1007/s11577-004-0119-8.

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Gaughan, Monica, Julia Melkers, and Eric Welch. "Differential Social Network Effects on Scholarly Productivity." Science, Technology, & Human Values 43, no. 3 (November 5, 2017): 570–99. http://dx.doi.org/10.1177/0162243917735900.

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Academic productivity is realized through resources obtained from professional networks in which scientists are embedded. Using a national survey of academic faculty in Science, Technology, Engineering, and Mathematics (STEM) fields across multiple institution types, we examine how the structure of professional networks affects scholarly productivity and how those effects may differ by race, ethnicity, and gender. We find that network size masks important differences in composition. Using negative binomial regression, we find that both the size and composition of professional networks affect scientific productivity, but bigger is not always better. We find that instrumental networks increase scholarly productivity, while advice networks reduce it. There are important interactive effects that are masked by modeling only direct effects. We find that white men are especially advantaged by instrumental networks, and women are especially advantaged by advice networks.

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Yaniklar, Cengiz. "Stability and Instability in the Friendship Networks of the Turkish White-Collar Class." Social Behavior and Personality: an international journal 40, no. 7 (August 1, 2012): 1123–35. http://dx.doi.org/10.2224/sbp.2012.40.7.1123.

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In this study, I analyzed changes over time in networks consisting of closest friends in the context of the Turkish white-collar class. I also examined how life events affect these changes. I collected data using interviews (conducted 5 years apart) with 32 male and 37 female respondents. The data show that a significant amount of change in the friendship networks was associated with certain life events; geographical mobility was the most important of these. The data also suggest that there are some significant gendered differences in friendships that change as a result of social circumstances.

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Kong, Hanzhang, Qinma Kang, Chao Liu, Wenquan Li, Hong He, and Yunfan Kang. "An improved label propagation algorithm based on node intimacy for community detection in networks." International Journal of Modern Physics B 32, no. 25 (October 8, 2018): 1850279. http://dx.doi.org/10.1142/s021797921850279x.

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Community detection in complex network analysis is a quite challenging problem spanning many applications in various disciplines such as biology, physics and social network. A large number of methods have been developed for this problem, among which the label propagation algorithm (LPA) has attracted much attention because of its advantages of nearly-linear running time and easy implementation. Nevertheless, the random updating order and tie-breaking strategy in LPA make the algorithm unstable and may even lead to the formation of a monster community. In this paper, an improved LPA called LPA-INTIM is proposed for solving the community detection problem. Firstly, an intimacy matrix is constructed using local topology information for measuring the intimacy between nodes. And then, the node importance is calculated to ensure that nodes are updated in a specific order. Finally, the label influence is evaluated for updating node label during the label propagation process. In addition, we introduce a novel tightness function to improve the stability of the proposed algorithm. By the comparison with the methods presented in the literatures, experimental results on real-world and synthetic networks show the efficiency and effectiveness of our proposed algorithm.

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Karimi Rizi, A., M. Naderi Dehkordi, and N. Nemat bakhsh. "SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels." Security and Communication Networks 2019 (November 6, 2019): 1–23. http://dx.doi.org/10.1155/2019/8171263.

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In social networks, preserving privacy and preserving correlation among sensitive labels are a matter of trade-off. This paper presents a supervised anonymization technique, SNI (social network immunization), to publish social networks having multiple sensitive labels with correlation. SNI publishes all sensitive labels without distorting them. It publishes sensitive labels along with innovative labels named “partial sensitive labels” in an immune graph and multiple supplementary trees. These graph and trees, by itself or with the combination of other objects, supply correlation among sensitive labels for membership analysis. We present a framework along with an algorithm for extracting the immune graph and supplementary trees. These graph and trees minimize the membership error rate for membership analysis. The practical evaluation of the cancer code label of individuals also indicates the effectiveness of the SNI method.

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Biegel, David E., Jay Magaziner, and Martha Baum. "Social Support Networks of White and Black Elderly People at Risk for Institutionalization." Health & Social Work 16, no. 4 (November 1991): 245–57. http://dx.doi.org/10.1093/hsw/16.4.245.

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Kelshall, Candyce. "Violent Transnational Social Movements and their Impact on Contemporary Social Conflict." Journal of Intelligence, Conflict, and Warfare 1, no. 3 (February 1, 2019): 20. http://dx.doi.org/10.21810/jicw.v1i3.840.

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This paper takes the perspective that violent transnational social movements (VTSMs) have profoundly impacted contemporary conflict scenarios. Social movements, underpinned by ideology, create partisan, transnational echo chambers, and communities, which are in the process of ‘changing the weather’ in contemporary social interactions. Transnational advocacy networks work in tandem to ‘create the message’ and perpetuate narratives. Where extremist dialogue crosses over into violence, we argue that a new form of conflict emerges. Such conflict does not have the preservation of the state as a territorially important factor or reference point, but rather, the preservation and promotion of a cultural identity. Where ‘other’ identities also co-exist, as in multicultural societies, these extremist views, and the crossover to violence from extremist rhetoric, arguably create a new type of warfare which we label fifth generation.

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Liao, Zhifang, Lite Gu, Xiaoping Fan, Yan Zhang, and Chuanqi Tang. "Detecting the Structural Hole for Social Communities Based on Conductance–Degree." Applied Sciences 10, no. 13 (June 29, 2020): 4525. http://dx.doi.org/10.3390/app10134525.

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It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge different communities and gain access to multiple sources of information flow. In this paper, we devised a structural hole detection algorithm, known as the Conductance–Degree structural hole detection algorithm (CD-SHA), which computes the conductance and degree score of a vertex to identify the structural hole spanners in social networks. Next, we proposed an improved label propagation algorithm based on conductance (C-LPA) to filter the jamming nodes, which have a high conductance and degree score but are not structural holes. Finally, we evaluated the performance of the algorithm on different real-world networks, and we calculated several metrics for both structural holes and communities. The experimental results show that the algorithm can detect the structural holes and communities accurately and efficiently.

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TOOMET, OTT, MARCO VAN DER LEIJ, and MEREDITH ROLFE. "Social networks and labor market inequality between ethnicities and races." Network Science 1, no. 3 (December 2013): 321–52. http://dx.doi.org/10.1017/nws.2013.20.

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AbstractThis paper analyzes the relationship between unexplained racial/ethnic wage differentials on the one hand and social network segregation, as measured by inbreeding homophily, on the other. Our analysis is based on both the US and Estonian surveys, supplemented with the Estonian telephone communication data. In the case of Estonia we consider the regional variation in economic performance of the Russian minority, and in the US case we consider the regional variation in black--white differentials. Our analysis finds a strong relationship between the size of the wage differential and network segregation: Regions with more segregated social networks exhibit larger unexplained wage gaps.

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