Community detection in large-scale social networks: state-of-the-art and future directions

Azaouzi, Mehdi; Rhouma, Delel; Romdhane, Lotfi

doi:10.1007/s13278-019-0566-x

Cited by 65 publications

(22 citation statements)

References 106 publications

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“…There are researchers from various fields engaged in the study of scientific research teams (Azaouzi, Rhouma, & Ben Romdhane, 2019), and numerous findings derived from different perspectives. This paper suggests a closer concentration on (1) prolific teams, as sometimes the "part" is as important as the "whole" and (2) measures about the stability of research teams, as different measures may affect the research object selection for a deeper analysis.…”

Section: Discussionmentioning

confidence: 99%

A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams

Zou

Wang

2021

Data and Information Management

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Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of Science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures.

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Section: Discussionmentioning

confidence: 99%

A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams

Zou

Wang

2021

Data and Information Management

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“…(Community): Community structure [ 48 ] is defined as the partition of network nodes into groups, within which nodes are densely connected while between which they are sparsely connected.…”

Section: Preliminariesmentioning

confidence: 99%

Parallel social behavior-based algorithm for identification of influential users in social network

2021

Self Cite

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Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user’s interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.

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“…The vertices and links between them indicate to which division they belong. These datasets have been used widely for study in various types of community detection techniques [5]. So, we have chosen these datasets for analysis and visualization using the networkx package of Python.…”

Section: Dataset and Parameters Usedmentioning

confidence: 99%

Analytical Modeling of Real-World Social Network Parameters

Monika

Mangat

2020

Lecture Notes in Networks and Systems

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A social network is an interactive platform for massive communication. While analyzing a social network, its pictorial representation as a graph is required to explore and visualize it. Graph theory concepts are applied to social networks which are helpful in numerical formulations also. Social network graph theory is applicable to formulate and study various statistical parameters that can help in analyzing the social patterns of these networks. Various Python implementation commands from importing data to the computation of data for network statistics like the clustering coefficient and the degree distribution of nodes are described in this paper. These network statistics are significant as they can be used to unveil differences in the pattern of interconnections in a social network.

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Community detection in large-scale social networks: state-of-the-art and future directions

Cited by 65 publications

References 106 publications

A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams

A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams

Parallel social behavior-based algorithm for identification of influential users in social network

Analytical Modeling of Real-World Social Network Parameters

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