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Graph Representation Learning for Popularity Prediction Problem: A Survey [article]

Tiantian Chen, Jianxiong Guo, Weili Wu
2022 arXiv   pre-print
used model and techniques: embedding-based methods and deep learning methods.  ...  On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models.  ...  For the nodelevel influence representation learning, it mainly used the ramework of DeepInf model [59] .  ... 
arXiv:2203.07632v1 fatcat:jvgzbmih2fcvvfqdu44nax2qpa

Modeling Information Diffusion with Sequential Interactive Hypergraphs

Hai Jin, Yao Wu, Hong Huang, Yu Song, Haohui Wei, Xuanhua Shi
2022 IEEE Transactions on Sustainable Computing  
On the one hand, Existing models only focus on the internal influence within the diffusion flow but ignore the external influence from the dissemination of other contents.  ...  To address these issues, we introduce the hypergraph structure and a sequential framework to model the complex interactions in social networks.  ...  Representation learning is widely used to learn the user embedding for further diffusion, such as EmbeddingIC [4], inf2Vec [5] and HID [7] .  ... 
doi:10.1109/tsusc.2022.3152366 fatcat:hkbfdppgm5cuxbp2zddouamowe

Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction [article]

Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han
2020 arXiv   pre-print
prediction models; we achieve massive gains for users with sparse activities, and users who lack direct social neighbors in seed sets.  ...  In this paper, we present a novel variational autoencoder framework (Inf-VAE) to jointly embed homophily and influence through proximity-preserving social and position-encoded temporal latent variables  ...  We model social homophily through latent social variables designed to encourage users with shared social neighborhoods to have similar latent representations.  ... 
arXiv:2001.00132v1 fatcat:sxojna4b4rbw3dkk6jlqdnerhy

Sparsity-aware neural user behavior modeling in online interaction platforms [article]

Aravind Sankar
2022 arXiv   pre-print
Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data.  ...  In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications.  ...  space of users. • Emb-IC [79] : an embedded cascade model that generalizes IC to learn user representations from partial orders of user activations. • Inf2vec [80] : an influence embedding method that  ... 
arXiv:2202.13491v1 fatcat:5lhvre4kpzao5ow7gvxa2qnwhq