Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

• URL: http://arxiv.org/abs/2204.08229v1
• Date: Mon, 18 Apr 2022 09:25:06 GMT
• Title: Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
• Authors: Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma
• Abstract summary: We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
• Score: 59.221668173521884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

Related papers

• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• DySuse: Susceptibility Estimation in Dynamic Social Networks [2.736093604280113]
  We propose a task, called susceptibility estimation in dynamic social networks, which is more realistic and valuable in real-world applications. We leverage a structural feature module to independently capture the structural information of influence diffusion on each single graph snapshot. Our framework is superior to the existing dynamic graph embedding models and has satisfactory prediction performance in multiple influence diffusion models.
  arXiv  Detail & Related papers  (2023-08-21T03:28:34Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching [9.16219929722585]
  Information-centric network (ICN) aims to proactively keep limited popular content at the edge of network based on predicted results. In this paper, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. We also propose an age of information (AoI) based attention mechanism to extract valuable historical information.
  arXiv  Detail & Related papers  (2022-08-18T02:57:17Z)
• Independent Asymmetric Embedding Model for Cascade Prediction on Social Network [0.49269463638915806]
  Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. We propose an independent asymmetric embedding method to learn social embedding for cascade prediction.
  arXiv  Detail & Related papers  (2021-05-18T05:40:38Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations [13.680949377743392]
  We address the problem of learning of a spreading model such that the predictions generated from this model are accurate. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model.
  arXiv  Detail & Related papers  (2020-07-13T17:58:21Z)
• Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps [40.58156024231199]
  We study a novel problem of explainable user engagement prediction for social network Apps. We design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training/inference time.
  arXiv  Detail & Related papers  (2020-06-10T02:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.