Predictive Analysis of Temporal and Overlapping Community Structures in Social Media

Shahriari, Mohsen; Gunashekar, Stephen; Domarus, Marven von; Klamma, Ralf

doi:10.1145/2872518.2889292

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…In this paper, five representative algorithms are chosen as baselines. The five algorithms are CFCR [10], SGCI [8], PATOC [9], node2vec [22], and LINE [23]. For the first two algorithms, since the author did not give the abbreviations of the algorithm, for the convenience of subsequent presentation, we have named these two algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…Most of the existing research simply calculates community features based on manually selected features and then uses classifiers to make predictions. Most of the differences among these algorithms lie in the way they calculate features [8][9][10][11][12][13][14][15]. For example, in references [8,11], the authors simply calculate features based on manual selection.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in references [8,11], the authors simply calculate features based on manual selection. In references [9,10], the authors take into account the influence of the leader node on the evolution of the community. In the reference [15], the author automatically selects relevant features through pre-training classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…First, the existing research does not consider the impact of the nodes with different features on the evolution of the community. To solve this problem, in references [9,10], the authors introduced leader nodes when calculating community features. However, in reality, there are not only leader nodes in the community, but also other types of nodes.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Layer Feature Fusion-Based Community Evolution Prediction

Wang

Liu

2022

Future Internet

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Analyzing and predicting community evolution has many important applications in criminology, sociology, and other fields. In community evolution prediction, most of the existing research is simply calculating the features of the community, and then predicting the evolution event through the classifier. However, these methods do not consider the complex characteristics of community evolution, and only predict the community’s evolution from a single level. To solve these problems, this paper proposes an algorithm called multi-layer feature fusion-based community evolution prediction, which obtains features from the community layer and node layer. The final community feature is the fusion of the two layer features. At the node layer, this paper proposes a global and local-based role-extraction algorithm. This algorithm can effectively discover different roles in the community. In this way, we can distinguish the influence of nodes with different characteristics on the community evolution. At the community layer, this paper proposes to use the community hypergraph to obtain the inter-community interaction relationship. After all the features are obtained, this paper trains a classifier through these features and uses them in community evolution prediction. The experimental results show that the algorithm proposed in this paper is better than other algorithms in terms of prediction effect.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Layer Feature Fusion-Based Community Evolution Prediction

Wang

Liu

2022

Future Internet

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“…However, another group of researchers considered social networks dynamic, mapped in a sequence consisting of intervals of static networks in such a way that communities are extracted from a period independent from the periods before and after. This type of analysis allows us to evaluate the changes in the behaviour of communities by comparing their structures in two consecutive periods [14,23,26].…”

Section: Introductionmentioning

confidence: 99%

PopNode-PopCom: Identifying The Popular Nodes to Predict the Popular Communities in Dynamic Social Networks

Khafaei

Taraghi

Patil

2022

Preprint

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Social networks play an important role in human life, and the study of communities within them is of particular importance. In this research, sustainable communities called popular communities have been introduced and studied. In this regard, the life of social networks was considered in time snapshots. Then, the communities that which at least 45% of their members were present the next time snapshot were considered popular communities. In the next step, the structure of popular communities and their distinguishing features from other communities as well as some of their structural features along with the other centrality features were introduced. Finally, using the theory of rough set, the importance of the structural features was examined to predict popular communities using the rules derived from this theory. Also, the importance of popular nodes introduced in this study in terms of, Integrated Value of Influence (IVI), Collective Influence (CF) and Spreading Score was compared with the maximum nodes of these values in each of the popular communities. In this study, we tested the performance of our proposed model on the actual Facebook database. We used rough set theory to generate the rules and model learning. Leader-Node, Popular-Node, Closeness, Eigen Centralization, Betweenness and Community Density were used as features and Popular Community was used as a label. In the experiment, 10-Fold Cross-Validation was used for model learning and Standard Voting was used as a classifier in the ROSETTA toolkit environment. The experiment has shown that if the average of an entire community is low on closeness, betweenness, and eigenvector centrality but with a high number of popular nodes, the chances of the community remaining popular increase. In addition, the high density of the community is not a good criterion for the community to remain popular at all. It was also shown that with the help of these features, the popular communities were predicted with almost 84% accuracy. On the other hand, the nodes with the maximum values of IVI, CF and Spreading Score were a subset of popular nodes. This shows the importance of popular nodes in social networks which introduced in this study.

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