TLP-Negcn
Temporal Link Prediction via Network Embedding and Graph Convolutional Networks
Bibliographic Data
| ID | 22108483 |
|---|---|
| Authors | Akshi Kumar (0000-0003-4263-7168, Goldsmiths University of London), Abhishek Mallik (0000-0002-5665-1836, Delhi Technological University), Sanjay Kumar (0000-0002-9239-2966, Delhi Technological University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 4454-4464 |
| Publication date | 2024-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3367231 |
| OpenAlex | W4392908054 |
| Language | EN |
| References cited | 28 |
Temporal link prediction (TLP) is a prominent problem in network analysis that focuses on predicting the existence of future connections or relationships between entities in a dynamic network over time. The predictive capabilities of existing models of TLP are often constrained due to their difficulty in adapting to the changes in dynamic network structures over time. In this article, an improved TLP model, denoted as TLP-NEGCN, is introduced by leveraging network embedding, graph convolutional networks (GCNs), and bidirectional long short-term memory (BiLSTM). This integration provides a robust model of TLP that leverages historical network structures and captures temporal dynamics leading to improved performances. We employ graph embedding with self-clustering (GEMSEC) to create lower dimensional vector representations for all nodes of the network at the initial timestamps. The node embeddings are fed into an iterative training process using GCNs across timestamps in the dataset. This process enhances the node embeddings by capturing the network's temporal dynamics and integrating neighborhood information. We obtain edge embeddings by concatenating the node embeddings of the end nodes of each edge, encapsulating the information about the relationships between nodes in the network. Subsequently, these edge embeddings are processed through a BiLSTM architecture to forecast upcoming links in the network. The performance of the proposed model is compared against several baselines and contemporary TLP models on various real-life temporal datasets. The obtained results based on various evaluation metrics demonstrate the superiority of the proposed work
Combinatorics · Computer network · Embedding · Graph · Graph theory · Advanced Graph Neural Networks · Complex Network Analysis Techniques · Computer Science · Human Mobility and Location-Based Analysis · Mathematics · Artificial Intelligence · Theoretical Computer Science
| Highly cited | No |
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