Community Detection in Networks using Node Attributes and Modularity

Asim, Yousra; Ghazal, Rubina; Naeem, Wajeeha; Majeed, Abdul; Raza, Basit; Malik, Ahmad Kamran

doi:10.14569/ijacsa.2017.080148

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…Louvain-OR-Attribute (LOA) and Louvain-AND-Attribute (LAA) are two approaches that are proposed in [3]. The authors combined the gain in modularity with the similarity of users' attributes to detect the communities.…”

Section: A Non-incremental Approachesmentioning

confidence: 99%

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Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

et al. 2021

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Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities' transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

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Section: A Non-incremental Approachesmentioning

confidence: 99%

“…2 , fb-msg network2 , and ia-yahoo-msg network3 , such that |V | and |E| denote the number of nodes and edges, and |msg| denotes the number of messages.This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/…”

mentioning

confidence: 99%

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

et al. 2021

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“…These methods incorporate attribute information into an optimization objective like the modularity. [5] injects an attribute based similarity measure into the modularity function; [1] combines the gain in the modularity with multiple common users' attributes as an integrated objective; I-Louvain algorithm [3] proposes inertia-based modularity to describe the similarity between nodes with numeric attributes, and adds the inertia-based modularity to the original modularity formula to form the new optimization objective.…”

Section: Related Workmentioning

confidence: 99%

A Late-Fusion Approach to Community Detection in Attributed Networks

Liu¹,

Largeron²,

Zaı̈ane³

et al. 2020

Lecture Notes in Computer Science

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The majority of research on community detection in attributed networks follows an "early fusion" approach, in which the structural and attribute information about the network are integrated together as the guide to community detection. In this paper, we propose an approach called late-fusion, which looks at this problem from a different perspective. We first exploit the network structure and node attributes separately to produce two different partitionings. Later on, we combine these two sets of communities via a fusion algorithm, where we introduce a parameter for weighting the importance given to each type of information: node connections and attribute values. Extensive experiments on various real and synthetic networks show that our latefusion approach can improve detection accuracy from using only network structure. Moreover, our approach runs significantly faster than other attributed community detection algorithms including early fusion ones.

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“…1 In graphtheoretical terms, sentence packaging can be thus viewed as an approximation of dense subgraph decomposition, which is a very prominent area of research in graph theory. It has been also studied in the context of numerous applications, including biomedicine (e.g., for protein interaction network (Bader and Hogue, 2003) or brain connectivity analysis (Hagmann et al, 2008)), web mining (Sarıyuece et al, 2015), influence analysis (Ugander et al, 2012), community detection (Asim et al, 2017), etc. Our model is inspired by the work on community detection.…”

Section: Introductionmentioning

confidence: 99%

“…Community detection aims to cluster a given social network (graph) into groups of tightly connected or similar vertices (Asim et al, 2017). The different algorithms which have been proposed are often adapted to the particular characteristics of the investigated network (Fortunato and Hric, 2016).…”

Section: Introductionmentioning

confidence: 99%

Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem

Shvets

Mille

Wanner

2018

Proceedings of the 11th International Conference on Natural Language Generation

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An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be "packaged" into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the Verb-Net/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F 1-score of 0.738.

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Community Detection in Networks using Node Attributes and Modularity

Cited by 7 publications

References 21 publications

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

A Late-Fusion Approach to Community Detection in Attributed Networks

Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem

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