Community detection in social networks based on information propagation and user engagement

Nian, Fuzhong; Luo, Li; Yu, Xuelong; Guo, Xin

doi:10.1142/s0217979221501198

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Numerous scholars have studied information spreading in social networks. Nian et al [16][17][18][19] considered the influence of various effects on the information spreading process and proposed the concept of a hybrid spreading model. Wu et al [20] investigated the dual-channel effect of information spreading based on the susceptible acceptable recovery (SAR) model, and explored the effect of both channels on the spreading of explosive information.…”

Section: Introductionmentioning

confidence: 99%

Topological phase transition in network spreading

Nian

Zhang²

2023

Chinese Phys. B

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This paper investigates information spreading from the perspective of topological phase transition. Firstly, a new hybrid network is constructed based on the small-world networks and scale-free networks. Secondly, the attention mechanism of online users in information spreading is studied from four aspects: social distance, individual influence, content richness, and individual activity, and a dynamic evolution model of “connecting with spreading” is designed. Eventually, numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution model. The simulation results show that topological structure and node influences in different networks have undergone phase transition, which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period. The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule. Furthermore, the simulation results are compared with the real data, which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks, verifying the validity of the model proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Topological phase transition in network spreading

Nian

Zhang²

2023

Chinese Phys. B

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Detecting Overlapping Communities in Complex Networks: An Evolutionary Label Propagation Approach

Saif

Samie

Hamzeh

2023

Int. J. Info. Tech. Dec. Mak.

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A challenging issue in complex network analysis is overlapping community detection, which has attracted many studies. Label Propagation Algorithm (LPA) is one of the famous studies to detect communities. But it has some weaknesses such as using local information and randomly choosing the sequences of processing nodes. We introduce Evolutionary Label Propagation Algorithm (ELPA) to solve these problems and improve accuracy. ELPA uses an intelligent search instead of randomly processing nodes and fuses local and global perspectives. The proposed ELPA is compared with several state-of-the-art algorithms on synthetic and real-world networks with different sizes, densities, and complexities. The results indicate that ELPA provides better results on most of the test instances. Therefore, ELPA is an accurate and efficient algorithm for detecting overlapping communities.

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Towards Exploring the Influence of Community Structures on Information Dissemination in Sina Weibo Networks

Zhang

Fang

Lin

et al. 2021

Discrete Dynamics in Nature and Society

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The power of online social networks to propagate information within communities and from one community to the next is undeniable. Both network structure and information propagation affect each other; they restrict and cooperate with each other. However, they can also dynamically reshape the network topology of user’s social relationship in this process. The above process ultimately forms a feedback loop: the network structure affects how information spreads, while information propagation reshapes network topologies, so both evolve in concert over time. Using information propagation trees (IPT) of posts from the Sina Weibo microblogging site, we conducted a null model-based analysis to determine the influence of community structures on information propagation. We first generated randomized copies of the IPTs and then mined community structures from the originals and copies for comparison. An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.

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Community detection in social networks based on information propagation and user engagement

Cited by 3 publications

References 29 publications

Topological phase transition in network spreading

Topological phase transition in network spreading

Detecting Overlapping Communities in Complex Networks: An Evolutionary Label Propagation Approach

Towards Exploring the Influence of Community Structures on Information Dissemination in Sina Weibo Networks

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