A homophilic and dynamic influence maximization strategy based on independent cascade model in social networks
Bibliographic record
Abstract
Influence maximization (IM) is crucial for recommendation systems and social networks. Previous research primarily focused on static networks, neglecting the homophily and dynamics inherent in real-world networks. This has led to inaccurate simulations of information spread and influence propagation between nodes, with traditional IM algorithms’ selected seed node sets failing to adapt to network evolution. To address this issue, this paper proposes a homophilic and dynamic influence maximization strategy based on independent cascade model (HDIM). Specifically, HDIM consists of two components: the seed node selection strategy that accounts for both homophily and dynamics (SSHD), and the independent cascade model based on influence homophily and dynamics (ICIHD). SSHD strictly constrains the proportions of different node types in the seed node set and can flexibly update the seed node set when the network structure changes. ICIHD redefines the propagation probabilities between nodes, adjusting them in response to changes in the network structure. Experimental results demonstrate HDIM’s excellent performance. Specifically, the influence range of HDIM exceeds that of state-of-the-art methods. Furthermore, the proportions of various activated nodes are closer to those in the original network.
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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 | 0.001 |
| 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) | 0.000 | 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".