A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Graph Representation Learning for Popularity Prediction Problem: A Survey
[article]
2022
arXiv
pre-print
Preserved Fulltext
Other Versions
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
2022
IEEE Transactions on Sustainable Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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