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. 2024 May 30;26(6):477.
doi: 10.3390/e26060477.

Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model

Xiaoli Huang  1 , Jingyu Li  1 , Yumiao Yuan  1
Affiliations

Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model

Xiaoli Huang et al. Entropy (Basel). .

Abstract

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of "Temporal Information Entropy (TIE)", integrating it into the Node2Vec algorithm's random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value.

Keywords: causality analysis; dynamic social networks; graph convolutional networks (GCNs); link prediction; node2vec; temporal information entropy (TIE).

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Dynamic social network example.
Figure 2
Figure 2
Hypothetical example of dynamic social network link prediction.
Figure 3
Figure 3
The flowchart of dynamic social network link prediction combining entropy, causality, and GCN models.
Figure 4
Figure 4
The flowchart of generated features combining the TIE and Node2Vec.
Figure 5
Figure 5
The flowchart of feature vector processing based on a causality analysis.
Figure 6
Figure 6
The flowchart based on specific GCN model training.
Figure 7
Figure 7
The influence of positive and negative sample proportions on the evaluation metrics obtained by our proposed method on different datasets.
Figure 8
Figure 8
Results of evaluation metrics after 200 iterations of different methods on the Email dataset.
Figure 9
Figure 9
Results of evaluation metrics after 200 iterations of different methods on the CollegeMsg dataset.
Figure 10
Figure 10
Results of evaluation metrics after 200 iterations of different methods on the Hypertext dataset.
See this image and copyright information in PMC

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