Dynamic Network Link Prediction by Learning Effective Subgraphs using CNN-LSTM
Bibliographic record
Abstract
Predicting the future link between nodes is a significant problem in social network analysis, known as Link Prediction (LP). Recently, dynamic network link prediction has attracted many researchers due to its valuable real-world applications. However, most methods fail to perform satisfying prediction accuracy in various types of networks because the dynamic LP in evolving networks is struggling with spatial and nonlinear transitional patterns. Besides this, existing methods mostly involve the whole network and target link for the LP process. It leads to high computational costs. This paper aims to address these issues by proposing a novel framework named DLP-LES using deep learning methods. DLP-LES uses common neighbors based subgraph of a target link and learns the transitional pattern of it for a given dynamic network. We extract a set of heuristic features of the evolving subgraph to gather additional information about the target link. In this way, we avoid examining the entire network. Additionally, our model introduces new mechanisms to reduce computational costs. DLP- LES generates a lookup table to keep the required information of links of the network and uses a hashing method to store and fetch link information. We propose an algorithm to construct feature matrices of the evolving subgraph to learn transitional link patterns. Our model transforms the dynamic link prediction to a video classification problem, and uses Convolutional Neural Networks with Long Short-Term Memory neural networks. To verify the effectiveness of DLP-LES, extensive experiments are carried out on five real-world dynamic networks. We compare those results against four network embedding methods and basic heuristic methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".