Community detection in attributed networks for global transfer market

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

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Exponentially Twisted Sampling for Centrality Analysis and Community Detection in Attributed Networks

IEEE Transactions on Network Science and Engineering ◽

10.1109/tnse.2018.2870671 ◽

2019 ◽

Vol 6 (4) ◽

pp. 684-697

Author(s):

Cheng-Hsun Chang ◽

Cheng-Shang Chang ◽

Chia-Tai Chang ◽

Duan-Shin Lee ◽

Ping-En Lu

Keyword(s):

Community Detection ◽

Centrality Analysis ◽

Attributed Networks

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Distance dynamics based overlapping semantic community detection for node-attributed networks

Computational Intelligence ◽

10.1111/coin.12324 ◽

2020 ◽

Author(s):

Heli Sun ◽

Xiaolin Jia ◽

Ruodan Huang ◽

Pei Wang ◽

Chenyu Wang ◽

...

Keyword(s):

Community Detection ◽

Attributed Networks

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NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Sensors ◽

10.3390/s19020260 ◽

2019 ◽

Vol 19 (2) ◽

pp. 260 ◽

Cited By ~ 4

Author(s):

Bingyang Huang ◽

Chaokun Wang ◽

Binbin Wang

Keyword(s):

Community Detection ◽

Real World ◽

State Of The Art ◽

Label Propagation ◽

Detection Methods ◽

Overlapping Communities ◽

Propagation Algorithm ◽

Pruning Strategy ◽

Node Similarity ◽

Attributed Networks

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

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