An approach to detect backbones of information diffusers among different communities of a social platform

Bonifazi, Gianluca; Cauteruccio, Francesco; Corradini, Enrico; Marchetti, Michele; Pierini, Alberto; Terracina, Giorgio; Ursino, Domenico; Virgili, Luca

doi:10.1016/j.datak.2022.102048

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…Zhou et al [13] studied the identification under the SEIR model and also took the Jordan center as the source estimation. In reference [14], the authors proposed an approach for the detection of possible information diffuser backbones among different communities in a social network. The approach is based on a new centrality measure called disseminator centrality.…”

Section: Related Workmentioning

confidence: 99%

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Dong,

Lu,

Tan

et al. 2024

Electronics

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This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators.

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Section: Related Workmentioning

confidence: 99%

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Dong,

Lu,

Tan

et al. 2024

Electronics

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“…Each social media account is a network node, and their relations constitute the entire policy communication network. Bonifazi et al provides a new centrality measure method to detect the backbone of different community information disseminators on social platforms [45], which helps to reveal the communication bridges and those at the center of communication. Network density and centrality are vital indicators to measure the overall network size.…”

Section: Social Network and Semantic Network Analysis For Policy Comm...mentioning

confidence: 99%

Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy

Dai

Cheng

Liu

2023

Systems

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The emergence of the Internet and social media provides a new platform for information diffusion, promoting the interaction among relatively independent participants in the opinion market and changing the balance of the intrinsic mechanism and external dynamics based on political communication. In this way, it is necessary to investigate the new interactive landscape of political communication and political discourse regarding digital media. In this study, we conduct a social and semantic network analysis of the dissemination and public opinion generation landscape of the COVID-19 “New Ten Articles” policy communication by the Chinese government, exploring the network relationships and emotional value interactions behind the contact of a new public policy in China. The results show that, in the political communication system, the influence of information’s position in the communication field has surpassed the information source impact, and the power of network opinion leaders is significant; the policy communication network presents a situation of “identity status circle” division, and the information circle connection presents a trend of fracture and barrier thickening, which may cause policy information alienation and social opinion polarization risk; the imbalance between policy information supply and public demand is further enhanced, and the negative emotion “cloud” is distributed on a scale and condenses into grassroots social governance pressure; and the content released by some key opinion leaders, experts, online media, and local mainstream media accounts is significantly correlated with network emotions. These emotions continue to spread in subsequent discussions, and to some extent, influence the formation process of political public opinion.

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“…One relevant research topic involves the detection of disseminators. Reference [18] presents the dissemination of information among communities by creating backbones among them. The authors propose a new centrality measure called disseminator centrality.…”

Section: Later Approachesmentioning

confidence: 99%

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Anderson,

Potikas,

Potika

2023

Information

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Community detection has been (and remains) a very important topic in several fields. From marketing and social networking to biological studies, community detection plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities (non-overlapping communities) but eventually moved forward to placing nodes in multiple communities (overlapping communities). Unfortunately, community detection has always been a time-inefficient process, and datasets are too large to realistically process them using traditional methods. Because of this, recent methods have turned to parallelism and graph stream models, where the edge list is accessed one edge at a time. However, all these methods, while offering a significant decrease in processing time, still have several shortcomings. We propose a new parallel algorithm called community detection with seed sets (CoDiS), which solves the overlapping community detection problem in graph streams. Initially, some nodes (seed sets) have known community structures, and the aim is to expand these communities by processing one edge at a time. The innovation of our approach is that it splits communities among the parallel computation workers so that each worker is only updating a subset of all the communities. By doing so, we decrease the edge processing throughput and decrease the amount of time each worker spends on each edge. Crucially, we remove the need for every worker to have access to every community. Experimental results show that we are able to gain a significant improvement in running time with no loss of accuracy.

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An approach to detect backbones of information diffusers among different communities of a social platform

Cited by 14 publications

References 32 publications

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Information Alienation and Circle Fracture: Policy Communication and Opinion-Generating Networks on Social Media in China from the Perspective of COVID-19 Policy

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

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