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Relevant bibliographies by topics / Node embeddings

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Academic literature on the topic 'Node embeddings'

Author: Grafiati

Published: 28 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Node embeddings"

1

Hu, Ganglin, and Jun Pang. "Relation-Aware Weighted Embedding for Heterogeneous Graphs." Information Technology and Control 52, no. 1 (2023): 199–214. http://dx.doi.org/10.5755/j01.itc.52.1.32390.

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Heterogeneous graph embedding, aiming to learn the low-dimensional representations of nodes, is effective in many tasks, such as link prediction, node classification, and community detection. Most existing graph embedding methods conducted on heterogeneous graphs treat the heterogeneous neighbours equally. Although it is possible to get node weights through attention mechanisms mainly developed using expensive recursive message-passing, they are difficult to deal with large-scale networks. In this paper, we propose R-WHGE, a relation-aware weighted embedding model for heterogeneous graphs, to

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Begga, Ahmed, Francisco Escolano Ruiz, and Miguel Ángel Lozano. "Edge-Centric Embeddings of Digraphs: Properties and Stability Under Sparsification." Entropy 27, no. 3 (2025): 304. https://doi.org/10.3390/e27030304.

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In this paper, we define and characterize the embedding of edges and higher-order entities in directed graphs (digraphs) and relate these embeddings to those of nodes. Our edge-centric approach consists of the following: (a) Embedding line digraphs (or their iterated versions); (b) Exploiting the rank properties of these embeddings to show that edge/path similarity can be posed as a linear combination of node similarities; (c) Solving scalability issues through digraph sparsification; (d) Evaluating the performance of these embeddings for classification and clustering. We commence by identifyi

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Jin, Junchen, Mark Heimann, Di Jin, and Danai Koutra. "Toward Understanding and Evaluating Structural Node Embeddings." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–32. http://dx.doi.org/10.1145/3481639.

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While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences , a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles—i.e., similar functions, ties or interactions with nodes in other positions—irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small s

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BOZKURT, ILKER NADI, HAI HUANG, BRUCE MAGGS, ANDRÉA RICHA, and MAVERICK WOO. "Mutual Embeddings." Journal of Interconnection Networks 15, no. 01n02 (2015): 1550001. http://dx.doi.org/10.1142/s0219265915500012.

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This paper introduces a type of graph embedding called a mutual embedding. A mutual embedding between two n-node graphs [Formula: see text] and [Formula: see text] is an identification of the vertices of V1 and V2, i.e., a bijection [Formula: see text], together with an embedding of G1 into G2 and an embedding of G2 into G1 where in the embedding of G1 into G2, each node u of G1 is mapped to π(u) in G2 and in the embedding of G2 into G1 each node v of G2 is mapped to [Formula: see text] in G1. The identification of vertices in G1 and G2 constrains the two embeddings so that it is not always po

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Zhou, Houquan, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng. "A Provable Framework of Learning Graph Embeddings via Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4946–53. http://dx.doi.org/10.1609/aaai.v37i4.25621.

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Given a large graph, can we learn its node embeddings from a smaller summary graph? What is the relationship between embeddings learned from original graphs and their summary graphs? Graph representation learning plays an important role in many graph mining applications, but learning em-beddings of large-scale graphs remains a challenge. Recent works try to alleviate it via graph summarization, which typ-ically includes the three steps: reducing the graph size by combining nodes and edges into supernodes and superedges,learning the supernode embedding on the summary graph and then restoring th

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Jing, Baoyu, Yuchen Yan, Kaize Ding, et al. "Sterling: Synergistic Representation Learning on Bipartite Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 12976–84. http://dx.doi.org/10.1609/aaai.v38i12.29195.

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A fundamental challenge of bipartite graph representation learning is how to extract informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to address this challenge. Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs. Contrastive learning usually requires a large number of negative node pairs, which could lead to computational burden and semantic errors. In this paper, we introduce a novel synergistic representation learning model (STERLING) to learn node embeddings w

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Monnin, Pierre, Chedy Raïssi, Amedeo Napoli, and Adrien Coulet. "Discovering alignment relations with Graph Convolutional Networks: A biomedical case study." Semantic Web 13, no. 3 (2022): 379–98. http://dx.doi.org/10.3233/sw-210452.

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Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task encompasses the identification, within an aggregated knowledge graph, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment r

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Cheng, Pengyu, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson, and Lawrence Carin. "Dynamic Embedding on Textual Networks via a Gaussian Process." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7562–69. http://dx.doi.org/10.1609/aaai.v34i05.6255.

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Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures; however, real-world networks are often dynamic. We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). After training, DetGP can be applied efficiently to dynamic graphs without re-training or backpropagation. The learned representation of each node is a combination of textual and structural embeddings. Because the struct

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Park, Chanyoung, Donghyun Kim, Jiawei Han, and Hwanjo Yu. "Unsupervised Attributed Multiplex Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5371–78. http://dx.doi.org/10.1609/aaai.v34i04.5985.

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Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and t

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Tian, Jiadong, Jiali Lin, and Dagang Li. "Edge and Node Enhancement Graph Convolutional Network: Imbalanced Graph Node Classification Method Based on Edge-Node Collaborative Enhancement." Mathematics 13, no. 7 (2025): 1038. https://doi.org/10.3390/math13071038.

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In addressing the issue of node classification with imbalanced data distribution, traditional models exhibit significant limitations. Conventional improvement methods, such as node replication or weight adjustment, often focus solely on nodes, neglecting connection relationships. However, numerous studies have demonstrated that optimizing edge distribution can improve the quality of node embeddings. In this paper, we propose the Edge and Node Collaborative Enhancement method (ENE-GCN). This method identifies potentially associated node pairs by similarity measures and constructs a hybrid adjac

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Dissertations / Theses on the topic "Node embeddings"

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Yandrapally, Aruna Harini. "Combining Node Embeddings From Multiple Contexts Using Multi Dimensional Scaling." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658906149105.

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Li, Mengzhen. "Integration of Node Embeddings for Multiple Versions of A Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1595435155975104.

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Sabo, Jozef. "Aplikace metody učení bez učitele na hledání podobných grafů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445517.

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Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one cla

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Wåhlin, Lova. "Towards Machine Learning Enabled Automatic Design of IT-Network Architectures." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249213.

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There are many machine learning techniques that cannot be performed on graph-data. Techniques such as graph embedding, i.e mapping a graph to a vector, can open up a variety of machine learning solutions. This thesis addresses to what extent static graph embedding techniques can capture important characteristics of an IT-architecture graph, with the purpose of embedding the graphs in a common euclidean vector space that can serve as the state space in a reinforcement learning setup. The metric used for evaluating the performance of the embedding is the security of the graph, i.e the time it wo

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Zhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.

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Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023/document.

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L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’all

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Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023.

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L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’all

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Lin, Christy. "Unsupervised random walk node embeddings for network block structure representation." Thesis, 2021. https://hdl.handle.net/2144/43083.

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There has been an explosion of network data in the physical, chemical, biological, computational, and social sciences in the last few decades. Node embeddings, i.e., Euclidean-space representations of nodes in a network, make it possible to apply to network data, tools and algorithms from multivariate statistics and machine learning that were developed for Euclidean-space data. Random walk node embeddings are a class of recently developed node embedding techniques where the vector representations are learned by optimizing objective functions involving skip-bigram statistics computed from rando

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Feng, Ming-Han, and 馮銘漢. "Multi-relational Network Embeddings Considering Link Structures and Node Attributes." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/466w9j.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>105<br>Multi-relational networks are ubiquitous in real world. It is, however, difficult to be analyzed due to the complex structure of the network. A plausible approach to analyze such network is to embed the entity information as an informative feature vector. However, present embedding methods either consider only single-relational information, or neglect the importance of structural information. In addition, some of them require fine-tuning of hyperparameters, which might not be feasible for an unsupervised embedding generation task. In this work we propose M

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Bandyopadhyay, Sambaran. "Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4646.

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Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also an ill-posed problem. There exist several heuristics and algorithms to compute the centrality of a node in a graph, but there is no formal definition of centrality available in the network science literature. Lately, researchers have proposed axiomatic frameworks for the centrality of a node in a network. However, these existing formal frameworks are not generic in nature in

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Books on the topic "Node embeddings"

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M. Le Marc Le Menestrel. A note on embedding von Neumann and Morgenstern utility theory in a qualitative context. INSEAD, 1998.

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Aggarwal, Manasvi, and M. N. Murty. Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs. Springer Singapore Pte. Limited, 2020.

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Book chapters on the topic "Node embeddings"

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Vu, Thuy, and D. Stott Parker. "Mining Community Structure with Node Embeddings." In Lecture Notes in Social Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51367-6_6.

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Meghashyam, P., and V. Susheela Devi. "Community Based Node Embeddings for Networks." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_41.

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Becker, Alexander, Mohamed Ahmed Sherif, and Axel-Cyrille Ngonga Ngomo. "Blink: Blank Node Matching Using Embeddings." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77844-5_12.

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Chekol, Melisachew Wudage, and Giuseppe Pirrò. "Refining Node Embeddings via Semantic Proximity." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62419-4_5.

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Wu, Changmin, Giannis Nikolentzos, and Michalis Vazirgiannis. "Matching Node Embeddings Using Valid Assignment Kernels." In Complex Networks and Their Applications VIII. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_67.

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Roy, Aman, Vinayak Kumar, Debdoot Mukherjee, and Tanmoy Chakraborty. "Learning Multigraph Node Embeddings Using Guided Lévy Flights." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_41.

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Bielak, Piotr, Daria Puchalska, and Tomasz Kajdanowicz. "Retrofitting Structural Graph Embeddings with Node Attribute Information." In Computational Science – ICCS 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08751-6_13.

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Lv, Xin, Jiaxin Shi, Shulin Cao, Lei Hou, and Juanzi Li. "Triple-as-Node Knowledge Graph and Its Embeddings." In Database Systems for Advanced Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00123-9_8.

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Lv, Xin, Jiaxin Shi, Shulin Cao, Lei Hou, and Juanzi Li. "Triple-as-Node Knowledge Graph and Its Embeddings." In Database Systems for Advanced Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00123-9_8.

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Dehghan, Ashkan, Kinga Siuta, Agata Skorupka, et al. "Unsupervised Framework for Evaluating Structural Node Embeddings of Graphs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32296-9_3.

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Conference papers on the topic "Node embeddings"

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Chen, Dehua, Tiago Pimentel, Adriano Veloso, and Nivio Ziviani. "Denoising Node Embeddings." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812033.

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Node embedding (NE) algorithms capture features of graph’s nodes and represent them in a low dimensional vector space. Graphs are inherently noisy structures, which might reduce the learned representations quality. We propose a novel approach using denoising autoencoders to reduce noise in the learned representation of nodes. Experiments with three state-of-the-art NE algorithms show that our approach effectively reduces noise in a link prediction task.

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Singer, Uriel, Ido Guy, and Kira Radinsky. "Node Embedding over Temporal Graphs." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/640.

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In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at di

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Angonese, Silvio Fernando, and Renata Galante. "Composition of Heterogeneous Node Embeddings - Unlocking the Power of Heterogeneous Graph Representation." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbbd.2024.243436.

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Heterogeneous graphs have high representation power, which can be maximized through node embeddings. Important embedding approaches are based on node features and node metapaths, applied individually. This paper proposes the creation of heterogeneous composition node embeddings, which are based on local node features, features from node neighbors, and node metapaths. This results in two types of composition embeddings: Features + Metapaths and Aggregated + Metapaths. Experiments have demonstrated superior performance compared to the baseline. In the experiments, our composition Aggregated Feat

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Angonese, Silvio Fernando, and Renata Galante. "Processing Heterogeneous Graphs within Heterogeneous Data Type Embeddings to Enhance Recommender Systems." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbbd_estendido.2024.243731.

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Embeddings represent a viable solution to address the challenge of data and information generation in heterogeneous graphs. This research presents our approach for generating and processing heterogeneous embeddings (AGHE), which are built from various data types such as text, images, and subgraphs embedded in nodes. AGHE comprises several stages, from graph creation to the generation of embedding compositions based on node features and metapaths. In the conducted experiments, simple and embedding compositions were used as input data for the Node Classification task in Recommender Systems, inve

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Celikkanat, Abdulkadir, and Fragkiskos D. Malliaros. "Kernel Node Embeddings." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969363.

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Luo, Dixin, Haoran Cheng, Qingbin Li, and Hongteng Xu. "Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/678.

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Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. In practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. Instead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to pri

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Hao, Yu, Xin Cao, Yixiang Fang, Xike Xie, and Sibo Wang. "Inductive Link Prediction for Nodes Having Only Attribute Information." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/168.

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Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which cons

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Huang, Hong, Ruize Shi, Wei Zhou, Xiao Wang, Hai Jin, and Xiaoming Fu. "Temporal Heterogeneous Information Network Embedding." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/203.

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Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE

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Dalmia, Ayushi, Ganesh J, and Manish Gupta. "Towards Interpretation of Node Embeddings." In Companion of the The Web Conference 2018. ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191523.

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Luo, Gongxu, Jianxin Li, Hao Peng, et al. "Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/381.

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Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with grap

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