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Joint nonnegative matrix factorization and network embedding for graph co-clustering

NEUROcoMPUTING 2021年 462卷 453-465页

作者： Wang, Yan Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol POB 1632 South TaiBai Rd Xian Shaanxi Peoples R China Xidian Univ Dept Lib Xian Shaanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

graph co-clustering aims to simultaneously group heterogeneous vertices in bipartite networks. The current algorithms measure similarity of vertices by either topology or latent feature of networks, which is insufficient to fully characterize the structure of bipartite graphs. To overcome this problem, we propose a novel co-clustering algorithm by jointly integrating network embedding and NMF (called NENMF) based on the fact that graph representation learning implicitly implies matrix factorizations, where multiple views of bipartite networks are integrated for graph co-clustering. Specifically, the equivalence between nonnegative matrix factorization (NMF) graph embedding for co-clustering is proven, which serves as the theoretical foundation for the proposed algorithm. Then, two auxiliary graphs are generated to fully characterize the topology structure of bipartite networks. Finally, NENMF jointly learns low-rank approximation matrices for bipartite networks and network embedding of auxiliary graphs, where network embedding is regularized into objective function of NMF. The main advantage of the proposed algorithm is to boost the accuracy by combining the low-dimensional approximation and graph representation of bipartite networks without increasing time complexity. The experimental results demonstrate that NENMF outperforms state-of-the-art approaches in terms of accuracy. (c) 2021 Elsevier B.V. All rights reserved.

关键词： graph co-clustering Bipartite networks Nonnegative matrix factorization Network embedding

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Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks

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SWARM AND EVOLUTIONARY coMPUTATION 2016年 26卷 137-156页

作者： Attea, Bara'a A. Hariz, Wisam A. Abdulhalim, Mayyadah F. Univ Baghdad Dept Comp Sci Baghdad Iraq Univ Bahrain Dept Comp Sci Al Zallaq Bahrain

Due to globalization, the characteristic of many systems in biology, engineering and sociology paradigms can nowadays be captured and investigated as networks of connected communities. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of multi-objective evolutionary algorithms (MOEAs) reveals outperformed results. Despite the existing efforts on designing effective multi-objective optimization (MOO) models and investigating the performance of several MOEAs for detecting natural community structures, their techniques lack the introduction of some problem-specific heuristic operators that realize their principles from the natural structure of communities. Moreover, most of these MOEAs evaluate and compare their performance under different algorithmic settings that may hold unmerited conclusions. The main contribution of this paper is two-fold. Firstly, to reformulate the community detection problem as a MOO model that can simultaneously capture the intra-and inter community structures. Secondly, to propose a heuristic perturbation operator that can emphasize the search for such intra-and inter-community connections in an attempt to offer a positive collaboration with the MOO model. One of the prominent multi-objective evolutionary algorithms (the so-called MOEA/D) is adopted with the proposed community detection model and the perturbation operator to identify the overlapped community sets in complex networks. Under the same MOEA/D characteristic settings, the performance of the proposed model and test results are evaluated against three state-of-the-art MOO models. The experiments on real-world and synthetic social networks of different complexities demonstrate the effectiveness of the proposed model to define community detection problem. Moreover, the results prove the positive impact of the proposed heuristic operator to harness the stren

关键词： community-less nodes graph co-clustering MOEA/D Non-dominated solution NP-hard Social networks

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