Relevant bibliographies by topics / White label social networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'White label social networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "White label social networks"

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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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Dissertations / Theses on the topic "White label social networks"

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Schnitzler, Peter. "Technologische Analysen im Umfeld Sozialer Netzwerke." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1226399874317-02633.

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Die vorliegende Arbeit analysiert die Möglichkeiten einer Daten- und Kontaktaggregation im Umfeld Sozialer Netzwerke. Zunächst wird eine Kategorisierung der wichtigsten Netzwerke und Frameworks vorgenommen. Die Funktionalitäten von acht Sozialen Netzwerken und fünf Frameworks werden anhand einer zuvor entwickelten Evaluierungsmatrix detailliert untersucht. Dabei stehen insbesondere die Funktionalitäten der APIs im Vordergrund. Aufbauend auf den Ergebnissen der Analyse wird ein Prototyp für eine Daten- und Kontaktaggregation konzipiert, implementiert und evaluiert. Abschließend werden Empfehlungen zu den verwendeten Technologien und für die Konzipierungen von zukünftigen Daten- und Kontaktaggregationen im Umfeld Sozialer Netzwerke gegeben
This thesis analyses the potential of a data- and contactaggreation in the context of social networks. It provides an overview and categorization of the most important networks and frameworks. The functions of eight social networks and five frameworks are analyzed on the basis of a previously developed matrix of evaluation. Special attention is paid to the features of the APIs. Using the results from the evaluation a prototype is planned, coded and evaluated. Finally, regards for future aggregations between social networks are elaborated
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Bolden, Christian. "Evolution of the Folk Devil: A Social Network Perspective of the Hybrid Gang Label." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3063.

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In keeping abreast of current gang phenomena, this study seeks to comparatively examine structural processes and characteristics of gangs in chronic gang city, San Antonio, and an emerging gang city that would be more likely to have "hybrid" gangs, Orlando. Hybrid gangs have been identified as having organizational processes that differ from traditional gangs; thus, this work will examine these processes that consist of a range of non-traditional phenomena, including cooperation between gangs, members switching gang affiliations, as well as gang initiations, and members leaving gangs. Additional characteristics uniquely associated with hybrid gangs consist of the notable presence of white, middle-class, and female gang members. Evidence suggests that the hybrid gang is more of a socially constructed moral panic than a reality. A limited number of recent studies have indicated that some gangs may better fit into a social network framework rather than a solid organizational analysis. Whe
Ph.D.
Department of Sociology
Sciences
Sociology PhD
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McLellan, Ruth. "Who influences white working-class boys' higher education decision-making process? : the role of social networks." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/367992/.

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The study illuminates the influence of social networks on the HE decision-making process of white working-class boys. The impact of gender, race and social class social characteristics on white working-class boys HE decision-making is assessed. In addition, how white working-class boys define and discuss the membership of their social network, together with the phenomenon of social network influence on white working-class boys’ decision-making about HE at Key Stage 4. The expansive literature review explores the problem that white working-class boys are seriously under-represented in HE and examines widening participation government policy, factors influencing the under-representation of boys in HE and the influences on the decision-making process for HE participation. The literature review concluded the strong impact of social characteristics and social relations on influencing white working-class boys to aspire to HE. The unique interpretive study is illuminated through a case study approach, which uses qualitative structured interviews with nine boys and their social network as a way of revealing the boys’ HE decision-making phenomenon. Interpretive analysis highlights the influence of social network and the boys’ perspectives specifically identified the processes of ‘talk’, ‘behaviour’, and ‘experience’. Analysis of social network data exposed their influence of talk, aspiration raising, and support for agency. The findings suggest that access to social capital resources pervades the ability of the social network to influence boys’ HE decision-making process. Mothers have a powerful and positive influence on their son’s aspirations for HE, but mothers with limited access to social networks with HE experience were limited in their ability to influence. Teachers all possess experience of HE, and those teachers who are prepared to share their social capital resources provide another valuable influence on boys’ HE decision-making process. The study concludes that the government policy for increasing white working-class boys’ HE participation must include provision for supporting mothers with raising and vitally enabling their son’s aspirations for HE. In addition, schools have a critical role to play on educating social networks and boys on the importance of developing and utilising social capital in order to ‘get ahead’. Furthermore, schools in disadvantaged areas already have experience, as a result of participating in ‘Aim higher’ (2004-2011) provision, of using cultural and social capital to support disadvantaged boys with their HE decision-making process. Schools knowledge of HE and networks could be further utilised to support their white-working class boys with their HE decision-making process.
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Top, Gustavson Aleta. "IDEOLOGICAL RESOURCE SHARING ON THE INTERNET AND THE PATTERNING OF NETWORKS IN THE WHITE SUPREMACIST/SEPARATIST MOVEMENT." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/dissertations/650.

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The Internet is a new tool for mobilization, communication, and articulation of social movement organizational framings of events and ideologies. The White Supremacist/Separatist Movement has had, and remains, a significant presence on the Internet. There are several hundred sites operating, representing almost every faction of the movement. Hyperlinks between sites allow the ideological resources (content of sites, online libraries, radio shows, etc.) offered by one group to be available to many groups, regardless of geography. Importantly, links are often asymmetrical and more prestigious sites have many "in" links. This movement has considerable diversity of beliefs, goals, tactics, and resources. Movements vary in the richness of symbolic resources available on their web sites. I operationalize "resource richness" as the amount and coverage of content on a website. Groups also exhibit a range of tactical orientations ranging from peaceful (education) to extremely violent (race war). Using network analysis, I investigate the structure of ties in the White Supremacist/Separatist Movement industry on the Internet. Through this method, analyses reveals patterns of sharing of ideological resources. I examine how ideological and tactical affinities structure the scope, density, and patterns of cybernetworks in the White Supremacist/Separatist Movement industry.
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Yu, Hsiang-Min, and 游詳閔. "Label Prediction on Dynamic Social Networks with Concept Drifting." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/sp455r.

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碩士
國立政治大學
資訊科學系
099
Label prediction is one of the central questions of social network research. The core of label prediction is the use of labeled nodes to predict labels of un-labeled nodes in a social network. The definition of a labeled social network is a social network of partial or complete labeled nodes. The nodes in the same social network have a mutual impact on each other’s labels. Previous research on label prediction have been focused on static social networks. However, social networks are more dynamic in reality. In a dynamic social network, the links of nodes, even the labels of nodes, can be changed with time. The mutual influence of nodes can also be changed. The changing is called “Concept Drift.” This thesis predicts the labels on a dynamic labeled social work. We address the problems of classification for a dynamic social network. The technique of label prediction on static social networks and algorithms used to tackle concept drift are combined to solve the label prediction problem on dynamic social networks. Experiments were performed on a labeled social network constructed from the Internet Movie Database. The results show that we can use the evolution of dynamic social networks to generate a more precise prediction of labels.
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Chen, Jheng-Huei, and 陳政輝. "Hybrid Search Scheme for Social Networks Supported by Dynamic Weighted Distributed Label Clustering." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/52739371484925555316.

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碩士
國立臺灣科技大學
電子工程系
101
Information searches are the most common application within social network. Choosing the appropriate users as the clusterheads in a clustering-based social network facilitates the effective spread of query messages, resulting in effective search performance. To accomplish this, a hybrid search(HS) scheme is proposed, using a dynamic weighted distributed label clustering (DW-DLC) structure to facilitate searches. The DW-DLC structure accelerates the process of locating target information within social network. The simulation results show that the HS+DW-DLC scheme obtains a favorable balance among the search success rate, searching delay, and the amount of repeated messages.
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Sudan, Nikita Maple. "Using social network information in recommender systems." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-3855.

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Recommender Systems are used to select online information relevant to a given user. Traditional (memory based) recommenders explore the user-item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly. With the growing popularity of social networks, recommender systems can benefit from combining history of user preferences with information from the social/trust network of users. This thesis explores two techniques of combining user-item rating history with trust network information to make better user-item rating predictions. The first approach (SCOAL [5]) simultaneously co-clusters and learns separate models for each co-cluster. The co-clustering is based on the user features as well as the rating history. This captures the intuition that certain groups of users have similar preferences for certain groups of items. The grouping of certain users is affected by the similarity in the rating behavior and the trust network. The second graph-based label propagation approach (MAD [27]) works in a transductive setting and propagates ratings of user-item pairs directly on the user social graph. We evaluate both approaches on two large public data-sets from Epinions.com and Flixster.com. The thesis is amongst the first to explore the role of distrust in rating prediction. Since distrust is not as transitive as trust i.e. an enemy's enemy need not be an enemy or a friend, distrust can't directly replace trust in trust propagation approaches. By using a low dimensional representation of the original trust network in SCOAL, we use distrust as it is and don't propagate it. Using SCOAL, we can pin-point the groups of users and the groups of items that have the same preference model. Both SCOAL and MAD are able to seamlessly integrate side information such as item-subject and item-author information into the trust based rating prediction model.
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Perkins, Robert Kelvin. "Racializing business networks : a study of the impact that social capital has on African American and White-dominated business networks /." 2004.

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Lee, Joohee. "Psychological health in Asian and Caucasian women who have experienced domestic violence: the role of ethnic background, social support, and coping." Thesis, 2004. http://hdl.handle.net/2152/1231.

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Books on the topic "White label social networks"

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The general sociology of Harrison C. White: Chaos and order in networks. New York: Palgrave Macmillan, 2005.

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More than a label: Why what you wear and who you're with doesn't define who you are. Minneapolis, MN: Free Spirit Pub., 2002.

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Caspi, David J. Ideologically motivated murder: The threat posed by white supremacist groups. El Paso: LFB Scholarly Publishing LLC, 2013.

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Transken, Si Chava. Working class women's friendships within the First Nations, Italian-Canadian and white Anglophone Canadian-born communities. [s.l: s.n.], 1992.

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Freie, Carrie. Class construction: White working-class student identity in the new millennium. Lanham, MD: Lexington Books, 2007.

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Snapp, Mary Beth. Occupational stress, social support, depression, and job dissatisfaction among black and white professioal-managerial women. Memphis, Tenn: Center for Research on Women, Dept. of Sociology and Social Work, Memphis State University, 1990.

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Psychedelic white: Goa trance and the viscosity of race. Minneapolis: University of Minnesota Press, 2007.

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Muharrar, Aisha. More Than a Label. Tandem Library, 2002.

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McGowen, Ernest. African Americans in White Suburbia: Social Networks and Political Behavior. University Press of Kansas, 2017.

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African Americans in White Suburbia: Social Networks and Political Behavior. University Press of Kansas, 2017.

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Book chapters on the topic "White label social networks"

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Wang, Ting, Xu Qian, and Xiaomeng Wang. "Label Propagation Based Community Detection Algorithm with Dpark." In Computational Social Networks, 116–27. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21786-4_10.

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Du, Juan, Feida Zhu, and Ee-Peng Lim. "Dynamic Label Propagation in Social Networks." In Database Systems for Advanced Applications, 194–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37450-0_14.

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Zhou, Yang, Ling Liu, Qi Zhang, Kisung Lee, and Balaji Palanisamy. "Enhancing Collaborative Filtering with Multi-label Classification." In Computational Data and Social Networks, 323–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34980-6_35.

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Liu, Chenyang, Dan Yin, Hao Li, Wei Wang, and Wu Yang. "Preserving Privacy in Social Networks Against Label Pair Attacks." In Wireless Algorithms, Systems, and Applications, 381–92. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60033-8_34.

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Kajdanowicz, Tomasz, Przemysław Kazienko, and Piotr Doskocz. "A Method of Label-Dependent Feature Extraction in Social Networks." In Computational Collective Intelligence. Technologies and Applications, 11–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16732-4_2.

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Kajdanowicz, Tomasz, Przemysław Kazienko, and Piotr Doskocz. "Label-Dependent Feature Extraction in Social Networks for Node Classification." In Lecture Notes in Computer Science, 89–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16567-2_7.

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Gallagher, Brian, and Tina Eliassi-Rad. "Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study." In Advances in Social Network Mining and Analysis, 1–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14929-0_1.

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Li, Xiang, Yijun Su, Neng Gao, Wei Tang, Ji Xiang, and Yuewu Wang. "Aligning Users Across Social Networks by Joint User and Label Consistence Representation." In Neural Information Processing, 656–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_55.

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Chen, Jui-Le, Jen-Wei Hu, and Chu-Sing Yang. "The Bridge Edge Label Propagation for Overlapping Community Detection in Social Networks." In Intelligent Data analysis and its Applications, Volume I, 97–102. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07776-5_11.

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Bahri, Leila, Barbara Carminati, Elena Ferrari, and William Lucia. "LAMP - Label-Based Access-Control for More Privacy in Online Social Networks." In Information Security Theory and Practice, 171–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45931-8_11.

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Conference papers on the topic "White label social networks"

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Liang, De-Ming, and Yu-Feng Li. "Lightweight Label Propagation for Large-Scale Network Data." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/475.

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Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.
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Wang, Yang, Fudong Qiu, Fan Wu, and Guihai Chen. "Resisting label-neighborhood attacks in outsourced social networks." In 2014 IEEE International Performance Computing and Communications Conference (IPCCC). IEEE, 2014. http://dx.doi.org/10.1109/pccc.2014.7017106.

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Song, Linmao, Yang Li, Shujia Fan, and Xiumei Fan. "Social-Based Multi-label Routing in Delay Tolerant Networks." In 2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud). IEEE, 2014. http://dx.doi.org/10.1109/bdcloud.2014.30.

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Dong, Hang, Wei Wang, Kaizhu Huang, and Frans Coenen. "Joint Multi-Label Attention Networks for Social Text Annotation." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1136.

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Attal, Jean-Philippe, and Maria Malek. "A New Label Propagation With Dams." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2808826.

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Asadi, Mohammad, and Foad Ghaderi. "Incremental Community Detection in Social Networks Using Label Propagation Method." In 2018 23rd Conference of Open Innovations Association (FRUCT). IEEE, 2018. http://dx.doi.org/10.23919/fruct.2018.8588023.

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Alzu'bi, Shadi, Omar Badarneh, Bilal Hawashin, Mahmoud Al-Ayyoub, Nouh Alhindawi, and Yaser Jararweh. "Multi-Label Emotion Classification for Arabic Tweets." In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931715.

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Hosseini, Razieh, and Reza Azmi. "Memory-based label propagation algorithm for community detection in social networks." In 2015 International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2015. http://dx.doi.org/10.1109/aisp.2015.7123488.

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Stanojevic, Marija, Jumanah Alshehri, and Zoran Obradovic. "Surveying public opinion using label prediction on social media data." In ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341161.3342861.

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Liu, Huawen, Zongjie Ma, Jianmin Zhao, and Zhonglong Zheng. "Penalized partial least squares for multi-label data." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921635.

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