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Towards Interpretation of Node Embeddings [PDF]
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018 Recently there have been a large number of studies on embedding large-scale information networks using low-dimensional, neighborhood and community aware node representations. Though the performance of these embedding models have been better than traditional methods for graph mining applications, little is known about what these representations encode ...
Ayushi Dalmia +2 more
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The Effects of Randomness on the Stability of Node Embeddings [PDF]
, 2021 We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorithms and graphs. We apply five node embeddings algorithms---HOPE, LINE, node2vec, SDNE, and GraphSAGE---to synthetic and empirical graphs and assess their stability under ...
Schumacher, Tobias +8 more
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Effective attributed network embedding with information behavior extraction [PDF]
PeerJ Computer Science, 2022 Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding ...
Ganglin Hu, Jun Pang, Xian Mo
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Semisupervised Community Preserving Network Embedding with Pairwise Constraints
Complexity, 2020 Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors.
Dong Liu +4 more
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Connection Science, 2023 Node importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges.
Zhixing Chen, Jian Shu, Linlan Liu
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2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019 Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification. Two popular approaches for this problem include matrix factorization and random walk-based models.
Çelikkanat, Abdulkadir +1 more
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A Systematic Evaluation of Node Embedding Robustness
CoRR, 2022 Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks. The popularity of these methods has grown significantly in recent years, yet, their robustness to perturbations of the input data is still poorly understood.
Alexandru Cristian Mara +3 more
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From Node Embedding To Community Embedding
CoRR, 2016 Code available at https://github.com/andompesta/nodeembedding-to ...
Vincent W. Zheng +4 more
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Multiplex Network Embedding Model with High-Order Node Dependence
Complexity, 2021 Multiplex networks have been widely used in information diffusion, social networks, transport, and biology multiomics. They contain multiple types of relations between nodes, in which each type of the relation is intuitively modeled as one layer.
Nianwen Ning +3 more
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A Novel Global Prototype-Based Node Embedding Technique
IEEE Access, 2022 Node embedding refers to learning or generating low-dimensional representations for nodes in a given graph. In the era of big data and large graphs, there has been a growing interest in node embedding across a wide range of applications, ranging from ...
Zyad Alkayem +4 more
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