An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Huang, Mingqing; Jiang, Qingshan; Qu, Qiang; Rasool, Abdur

doi:10.3390/sym13050869

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…There are several approaches to community detection including mathematical models, networks, modularity, and evolutionary computing. Mathematical models are formal representations of a system that are described in terms of mathematical equations or algorithms, such as statistics 5 , matrix factorization 6 , and fuzzy 7 . The network approach is a strategy used to analyze and understand the structure and behavior of a network, such as local communities 8 , network embedding 9 , and cliques 10 .…”

Section: Related Workmentioning

confidence: 99%

Community detection with Greedy Modularity disassembly strategy

Rustamaji,

Kusuma,

Nurdiati

et al. 2024

Sci Rep

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Community detection recognizes groups of densely connected nodes across networks, one of the fundamental procedures in network analysis. This research boosts the standard but locally optimized Greedy Modularity algorithm for community detection. We introduce innovative exploration techniques that include a variety of node and community disassembly strategies. These strategies include methods like non-triad creating, feeble, random as well as inadequate embeddedness for nodes, as well as low internal edge density, low triad participation ratio, weak, low conductance as well as random tactics for communities. We present a methodology that showcases the improvement in modularity across the wide variety of real-world and synthetic networks over the standard approaches. A detailed comparison against other well-known community detection algorithms further illustrates the better performance of our improved method. This study not only optimizes the process of community detection but also broadens the scope for a more nuanced and effective network analysis that may pave the way for more insights as to the dynamism and structures of its functioning by effectively addressing and overcoming the limitations that are inherently attached with the existing community detection algorithms.

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Section: Related Workmentioning

confidence: 99%

Community detection with Greedy Modularity disassembly strategy

Rustamaji,

Kusuma,

Nurdiati

et al. 2024

Sci Rep

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“…Lin H et al [32] proposed an algorithm for detecting overlapping communities in social networks using an augmented attribute graph and an improved weight adjustment strategy, demonstrating its effectiveness through extensive experiments on synthetic and real-world datasets. Huang M et al [33] proposed approach, ESNMF, utilizes symmetric non-negative matrix factorization and outperforms existing methods for community detection in large-scale networks by dividing the network into sub-graphs and extracting precise communities through non-negative matrix factorization. Peng Y et al [34] proposed the DSNE algorithm and community density metric (D), which offers an effective approach for detecting overlapping communities in bipartite networks, outperforming existing algorithms and providing a more comprehensive evaluation of community structures compared to modularity.…”

Section: Overlapping Community Detectionmentioning

confidence: 99%

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

et al. 2023

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Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks.

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“…ESNMF (Ego-Splitting networks using symmetric Nonnegative Matrix Factorization) [27] follows the similar idea of optimization-based factorization for overlapping community detection. Instead of factorizing the adjacency matrix of the large-scale graph, ESNMF partitions it into connected subgraphs via an ego-splitting process and incorporates prior information to obtain a subgraph matrix Âsub .…”

Section: Structural Matrix Factorizationmentioning

confidence: 99%

“…Let 𝑻 be a transition matrix in PageRank, TADW attempts to approximate 𝑻 + 𝑻 2 /2 ≈ 𝑴 T 1 𝑸 𝑿 text , wherein 𝑴 1 and 𝑸 are low-rank matrices. Similar to [26,27,29], the approximation is casted to minimization problem min 𝑴…”

Section: Structural Matrix Factorizationmentioning

confidence: 99%

Graph Structure and Isomorphism Learning: A Survey

Ho¹

2022

ICT Research

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With the great success of artificial intelligence in recent years, graph learning is gaining attention from both academia and industry [1, 2]. The power of graph data is its capacity to represent numerous complicated structures in a broad spectrum of application domains including protein networks, social networks, food webs, molecular structures, knowledge graphs, sentence dependency trees, and scene graphs of images. However, designing an effective graph learning architecture on arbitrary graphs is still an on-going research topic because of two challenges of learning complex topological structures of graphs and their nature of isomorphism. In this work, we aim to summarize and discuss the latest methods in graph learning, with special attention to two aspects of structure learning and permutation invariance learning. The survey starts by reviewing basic concepts on graph theory and graph signal processing. Next, we provide systematic categorization of graph learning methods to address two aspects above respectively. Finally, we conclude our paper with discussions and open issues in research and practice.

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An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Cited by 7 publications

References 41 publications

Community detection with Greedy Modularity disassembly strategy

Community detection with Greedy Modularity disassembly strategy

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

Graph Structure and Isomorphism Learning: A Survey

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