Low-Rank Representations Towards Classification Problem of Complex Networks
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Abstract (arXiv)
Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constructing a low-dimensional Euclidean embedding of the vertices of the network, where proximity of the vertices in the Euclidean space hints the likelihood of an edge (link). In this work, we study the performance of such low-rank representations of real-life networks on a network classification problem.
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References · 10
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2014DeepWalk: Online Learning of Social Representationscited by 1,543
- 2015LINE: Large-scale Information Network Embeddingcited by 582
- 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
- 2019What graph neural networks cannot learn: depth vs widthcited by 195
- 2020The impossibility of low rank representations for triangle-rich complex networkscited by 32
- 2020Node Embeddings and Exact Low-Rank Representations of Complex Networkscited by 21
- 2022Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labelingcited by 6
- 2017Local Algorithms for Hierarchical Dense Subgraph Discoverycited by 1
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