DeepInf: Social Influence Prediction with Deep Learning
Open accessAbstract
Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising.
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This argument has been pointed out in several papers, where it is proposed to improve conventional graph attention mechanisms by projecting the concatenated features once onto the learning matrix.
“This argument has already been pointed out in several papers, e.g., in [91][92][93], where it is proposed to improve the performance of conventional graph attention mechanisms by projecting the concatenated features once onto the learning matrix.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [91]
Section 3.2 (p. 4) describes attention computed from concatenated, transformed node features, so the source supports the claim’s reference to a concatenation-based graph-attention mechanism. It presents this as following Veličković et al., however, and does not propose projecting concatenated features once as an improvement over conventional GAT. Section 5.2 (p. 7) instead reports that the concatenation-based mechanism outperformed the alternative attention mechanisms the authors tried. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSeveral GAT variants have been developed to overcome GAT limitations using strategies such as multi-headed attention, restricting aggregation to top-K neighbors, and reordering GAT operations.
“Some notable examples are the use of multi-headed attention mechanisms at each node of the GNN [91], the restriction of aggregation to the neighbors with the highest K-attention scores [92], and the reordering of GAT operations [93].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [91]
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- Authors
- Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., & Tang, J.
- Affiliations
- Tsinghua University (CN)
- HEC Montréal (CA)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 2110-2119
- OpenAlex
- W2809583854
- Cited by
- 587
- References
- 63
- Citations by year
- 2026: 31 · 2025: 78 · 2024: 76 · 2023: 74 · 2022: 87 · 2021: 133 · 2020: 79 · 2019: 27 · 2018: 2
- FWCI
- 88.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- NSFC (61561130160)
- National Basic Research Program of China (2015CB358700)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- social networks, information dissemination, social media, user behavior, online recommendation, advertising
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