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Zeitschriftenartikel zum Thema „Node embeddings“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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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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Hou, Yuchen, and Lawrence B. Holder. "On Graph Mining With Deep Learning: Introducing Model R for Link Weight Prediction." Journal of Artificial Intelligence and Soft Computing Research 9, no. 1 (2019): 21–40. http://dx.doi.org/10.2478/jaiscr-2018-0022.

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Abstract Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of

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Wang, Lili, Chenghan Huang, Ying Lu, Weicheng Ma, Ruibo Liu, and Soroush Vosoughi. "Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks." ACM Transactions on Information Systems 40, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3472955.

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Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynami

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Wang, Yueyang, Ziheng Duan, Binbing Liao, Fei Wu, and Yueting Zhuang. "Heterogeneous Attributed Network Embedding with Graph Convolutional Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10061–62. http://dx.doi.org/10.1609/aaai.v33i01.330110061.

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Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate h

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Kutzkov, Konstantin. "LoNe Sampler: Graph Node Embeddings by Coordinated Local Neighborhood Sampling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8413–20. http://dx.doi.org/10.1609/aaai.v37i7.26014.

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Local graph neighborhood sampling is a fundamental computational problem that is at the heart of algorithms for node representation learning. Several works have presented algorithms for learning discrete node embeddings where graph nodes are represented by discrete features such as attributes of neighborhood nodes. Discrete embeddings offer several advantages compared to continuous word2vec-like node embeddings: ease of computation, scalability, and interpretability. We present LoNe Sampler, a suite of algorithms for generating discrete node embeddings by Local Neighborhood Sampling, and addre

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He, Tao, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, and Yuanfang Li. "SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4091–98. http://dx.doi.org/10.1609/aaai.v34i04.5832.

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Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is

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Zhong, Jianan, Hongjun Qiu, and Benyun Shi. "Dynamics-Preserving Graph Embedding for Community Mining and Network Immunization." Information 11, no. 5 (2020): 250. http://dx.doi.org/10.3390/info11050250.

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In recent years, the graph embedding approach has drawn a lot of attention in the field of network representation and analytics, the purpose of which is to automatically encode network elements into a low-dimensional vector space by preserving certain structural properties. On this basis, downstream machine learning methods can be implemented to solve static network analytic tasks, for example, node clustering based on community-preserving embeddings. However, by focusing only on structural properties, it would be difficult to characterize and manipulate various dynamics operating on the netwo

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Xie, Chengxin, Jingui Huang, Yongjiang Shi, Hui Pang, Liting Gao, and Xiumei Wen. "Ensemble graph auto-encoders for clustering and link prediction." PeerJ Computer Science 11 (January 22, 2025): e2648. https://doi.org/10.7717/peerj-cs.2648.

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Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood information for each node, yielding node embedding representations. However, existing graph auto-encoder models often overlook node representations and fail to capture contextual node information within the graph data, resulting in poor embedding effects. Accordingly, this study proposes the ensemble graph auto-encode

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Daradkeh, Mohammad. "A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 4 (2022): 186. http://dx.doi.org/10.3390/joitmc8040186.

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The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion

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Huang, Junjie, Huawei Shen, Liang Hou, and Xueqi Cheng. "SDGNN: Learning Node Representation for Signed Directed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.

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Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies o

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Sun, Zeyu, Wenjie Zhang, Lili Mou, Qihao Zhu, Yingfei Xiong, and Lu Zhang. "Generalized Equivariance and Preferential Labeling for GNN Node Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8395–403. http://dx.doi.org/10.1609/aaai.v36i8.20815.

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Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally

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Zhang, Chengdong, Keke Li, Shaoqing Wang, Bin Zhou, Lei Wang, and Fuzhen Sun. "Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction." Mathematics 11, no. 3 (2023): 578. http://dx.doi.org/10.3390/math11030578.

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Along with the growth of graph neural networks (GNNs), many researchers have adopted metapath-based GNNs to handle complex heterogeneous graph embedding. The conventional definition of a metapath only distinguishes whether there is a connection between nodes in the network schema, where the type of edge is ignored. This leads to inaccurate node representation and subsequently results in suboptimal prediction performance. In heterogeneous graphs, a node can be connected by multiple types of edges. In fact, each type of edge represents one kind of scene. The intuition is that if the embedding of

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Celikkanat, Abdulkadir, and Fragkiskos D. Malliaros. "Exponential Family Graph Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3357–64. http://dx.doi.org/10.1609/aaai.v34i04.5737.

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Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding mo

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Zhou, Sheng, Xin Wang, Jiajun Bu, et al. "DGE: Deep Generative Network Embedding Based on Commonality and Individuality." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6949–56. http://dx.doi.org/10.1609/aaai.v34i04.6178.

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Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes

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Shang, Chao, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. "End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3060–67. http://dx.doi.org/10.1609/aaai.v33i01.33013060.

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Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing grap

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Chen, Lvjia, and Shangsong Liang. "Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational Networks." Journal of Physics: Conference Series 2253, no. 1 (2022): 012038. http://dx.doi.org/10.1088/1742-6596/2253/1/012038.

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Abstract A dynamic network is often represented as a sequence of snapshots evolving over time. In certain real-world scenarios, the identities of nodes in snapshots of a dynamic network are unknown and need to be figured out. To deal with such a challenge, recently, the task of cross-temporal snapshot alignment for dynamic networks is proposed, which aims to match equivalent nodes across temporal snapshots of a dynamic network given a small set of identified nodes. However, in many dynamic multi-relational networks like temporal knowledge graphs, the relation type information of edges, which c

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Song, Yifan, Xiaolong Chen, Wenqing Lin, et al. "Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph." Proceedings of the VLDB Endowment 18, no. 4 (2024): 929–42. https://doi.org/10.14778/3717755.3717756.

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Graph embedding aims at mapping each node to a low-dimensional vector, beneficial for various applications like pattern matching, retrieval augmented generation and recommendation. In this paper, we study the large-scale temporal graph embedding problem. Different from simple graphs, each edge has a timestamp in temporal graphs, which requires the embeddings to encode the temporal biases. Factorizing similarity matrix is a common approach for generating simple graph embeddings where similarity can be well characterized by some conventional metrics like personalized PageRank. However, how to co

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Wang, Zheng, Yuexin Wu, Yang Bao, Jing Yu, and Xiaohui Wang. "Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation." Wireless Communications and Mobile Computing 2021 (February 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/6654349.

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Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple i

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Makarov, Ilya, Mikhail Makarov, and Dmitrii Kiselev. "Fusion of text and graph information for machine learning problems on networks." PeerJ Computer Science 7 (May 11, 2021): e526. http://dx.doi.org/10.7717/peerj-cs.526.

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Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract

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Altuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning." Applied Sciences 14, no. 2 (2024): 775. http://dx.doi.org/10.3390/app14020775.

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Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful

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Wei, Shaohan. "Multi-angle information aggregation for inductive temporal graph embedding." PeerJ Computer Science 10 (November 26, 2024): e2560. http://dx.doi.org/10.7717/peerj-cs.2560.

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Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This approach is not well-suited for temporal graphs that undergo continuous changes with the addition of new nodes and interactions. To address this limitation, we propose an inductive temporal graph embedding method called MIAN (Multi-angle Information Aggregation Network). The key fo

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Zhan, Junjian, Feng Li, Yang Wang, Daoyu Lin, and Guangluan Xu. "Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding." Applied Sciences 11, no. 5 (2021): 2371. http://dx.doi.org/10.3390/app11052371.

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As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model capture

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Zhong, Fengzhe, Yan Liu, Lian Liu, Guangsheng Zhang, and Shunran Duan. "DEDGCN: Dual Evolving Dynamic Graph Convolutional Network." Security and Communication Networks 2022 (May 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/6945397.

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With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars’ attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and

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Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Scalable robust graph embedding with Spark." Proceedings of the VLDB Endowment 15, no. 4 (2021): 914–22. http://dx.doi.org/10.14778/3503585.3503599.

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embeddin

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Bandyopadhyay, Sambaran, N. Lokesh, and M. N. Murty. "Outlier Aware Network Embedding for Attributed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 12–19. http://dx.doi.org/10.1609/aaai.v33i01.330112.

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Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expected. But real world networks often have anomalous nodes. Typically these outliers, being relatively unexplainable, affect the embeddings of other nodes in the network. Thus all the downstream network mining tasks fail miserably in the presence of such outliers. Hence an integrated

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Fionda, Valeria, and Giuseppe Pirrò. "Learning Triple Embeddings from Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3874–81. http://dx.doi.org/10.1609/aaai.v34i04.5800.

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Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the

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Makarov, Ilya, Dmitrii Kiselev, Nikita Nikitinsky, and Lovro Subelj. "Survey on graph embeddings and their applications to machine learning problems on graphs." PeerJ Computer Science 7 (February 4, 2021): e357. http://dx.doi.org/10.7717/peerj-cs.357.

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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graph

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Merchant, Arpit, Aristides Gionis, and Michael Mathioudakis. "Succinct graph representations as distance oracles." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2297–306. http://dx.doi.org/10.14778/3551793.3551794.

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Distance oracles answer shortest-path queries between any pair of nodes in a graph. They are often built using succinct graph representations such as spanners, sketches, and compressors to minimize oracle size and query answering latency. Node embeddings, in particular, offer graph representations that place adjacent nodes nearby each other in a low-rank space. However, their use in the design of distance oracles has not been sufficiently studied. In this paper, we empirically compare exact distance oracles constructed based on a variety of node embeddings and other succinct representations. W

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Sheng, Jinfang, Zili Yang, Bin Wang, and Yu Chen. "Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms." Mathematics 12, no. 5 (2024): 697. http://dx.doi.org/10.3390/math12050697.

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Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different

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Zhuo, Wei, Qianyi Zhan, Yuan Liu, Zhenping Xie, and Jing Lu. "Context Attention Heterogeneous Network Embedding." Computational Intelligence and Neuroscience 2019 (August 21, 2019): 1–15. http://dx.doi.org/10.1155/2019/8106073.

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Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimens

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Yuhong Zhao, Yuhong Zhao, Xiangming Ni Yuhong Zhao, Yue Yao Xiangming Ni, and Peng Mei Yue Yao. "Research on Link Prediction Method Based on Information Fusion Graph Embedding." 電腦學刊 35, no. 4 (2024): 059–73. http://dx.doi.org/10.53106/199115992024083504005.

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<p>To accurately and efficiently capture the topological and attribute information of nodes and apply them to the link prediction task, this paper proposes a Dual Channel Graph Convolution Link Prediction (DC-GCN). DC-GCN constructs a dual channel through the graph convolution network. DC-GCN can learn both topological embeddings and attribute embeddings of nodes; it introduces an attention mechanism to learn the weights of each embedding adaptively and then performs weighted fusion to obtain the final embedding representation of nodes. Finally, the Hadamard distance of nodes is used to

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Trouli, Georgia Eirini, Nikos Papadakis, and Haridimos Kondylakis. "Constructing Semantic Summaries Using Embeddings." Information 15, no. 4 (2024): 238. http://dx.doi.org/10.3390/info15040238.

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The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have

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Mel, Ahmad, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. "FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings." Applied Sciences 11, no. 21 (2021): 9884. http://dx.doi.org/10.3390/app11219884.

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Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node dis

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Fu, Kang, Guanghui Yan, Hao Luo, Wenwen Chang, and Jingwen Li. "Research on a Link Prediction Algorithm Based on Hypergraph Representation Learning." Electronics 12, no. 23 (2023): 4842. http://dx.doi.org/10.3390/electronics12234842.

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Link prediction is a crucial area of study within complex networks research. Mapping nodes to low-dimensional vectors through network embeddings is a vital technique for link prediction. Most of the existing methods employ “node–edge”-structured networks to model the data and learn node embeddings. In this paper, we initially introduce the Clique structure to enhance the existing model and investigate the impact of introducing two Clique structures (LECON: Learning Embedding based on Clique Of the Network) and nine motifs (LEMON: Learning Embedding based on Motif Of the Network), respectively,

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Liang, Shangsong, Zhuo Ouyang, and Zaiqiao Meng. "A Normalizing Flow-Based Co-Embedding Model for Attributed Networks." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–31. http://dx.doi.org/10.1145/3477049.

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Network embedding is a technique that aims at inferring the low-dimensional representations of nodes in a semantic space. In this article, we study the problem of inferring the low-dimensional representations of both nodes and attributes for attributed networks in the same semantic space such that the affinity between a node and an attribute can be effectively measured. Intuitively, this problem can be addressed by simply utilizing existing variational auto-encoder (VAE) based network embedding algorithms. However, the variational posterior distribution in previous VAE based network embedding

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Sarkar, Arindam, Nikhil Mehta, and Piyush Rai. "Graph Representation Learning via Ladder Gamma Variational Autoencoders." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5604–11. http://dx.doi.org/10.1609/aaai.v34i04.6013.

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We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. We model each node in the graph via a deep hierarchy of gamma-distributed embeddings, and define each link probability via a nonlinear function of the bottom-most layer's embeddings of its associated nodes. In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder VAE archi

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Zhou, Jingya, Ling Liu, Wenqi Wei, and Jianxi Fan. "Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding." ACM Computing Surveys 55, no. 2 (2023): 1–35. http://dx.doi.org/10.1145/3491206.

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Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Dozens of NRL algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This article reviews the design principles and the different node embedding techniques for NRL over homogeneous networks. To facilitate the comparison of diffe

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Tsugawa, Sho, and Hiroyuki Ohsaki. "Exploring Unknown Social Networks for Discovering Hidden Nodes." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1937–51. https://doi.org/10.1609/icwsm.v19i1.35911.

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In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of gr

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Tsitsulin, Anton, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan Oseledets, and Emmanuel Müller. "FREDE." Proceedings of the VLDB Endowment 14, no. 6 (2021): 1102–10. http://dx.doi.org/10.14778/3447689.3447713.

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Low-dimensional representations, or embeddings , of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or implicitly, on a similarity measure among nodes, they require the computation of a quadratic similarity matrix, inducing a tradeoff between space complexity and embedding quality. To date, no graph embedding work combines (i) linear space complexity, (ii) a nonlinear transform as its basis, and (iii) nontrivial quality guarantees. In this paper we introduce FREDE ( FREquent Directions Embedding ), a graph embedding base

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Hu, Fang, Liuhuan Li, Xiaoyu Huang, Xingyu Yan, and Panpan Huang. "Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis." JMIR Medical Informatics 8, no. 4 (2020): e16749. http://dx.doi.org/10.2196/16749.

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Background Recent research in machine-learning techniques has led to signiﬁcant progress in various research ﬁelds. In particular, knowledge discovery using this method has become a hot topic in traditional Chinese medicine. As the key clinical manifestations of patients, symptoms play a signiﬁcant role in clinical diagnosis and treatment, which evidently have their underlying traditional Chinese medicine mechanisms. Objective We aimed to explore the core symptoms and potential regularity of symptoms for diagnosing insomnia to reveal the key symptoms, hidden relationships underlying the sympto

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Wu, Xueyi, Yuanyuan Xu, Wenjie Zhang, and Ying Zhang. "Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach." Proceedings of the VLDB Endowment 17, no. 2 (2023): 175–83. http://dx.doi.org/10.14778/3626292.3626300.

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Bipartite graph embedding (BGE), as the fundamental task in bipartite network analysis, is to map each node to compact low-dimensional vectors that preserve intrinsic properties. The existing solutions towards BGE fall into two groups: metric-based methods and graph neural network-based (GNN-based) methods. The latter typically generates higher-quality embeddings than the former due to the strong representation ability of deep learning. Nevertheless, none of the existing GNN-based methods can handle billion-scale bipartite graphs due to the expensive message passing or complex modelling choice

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