Recurrent Graph Neural Networks for Rumor Detection in Online Forums
- URL: http://arxiv.org/abs/2108.03548v1
- Date: Sun, 8 Aug 2021 01:34:49 GMT
- Title: Recurrent Graph Neural Networks for Rumor Detection in Online Forums
- Authors: Di Huang, Jacob Bartel, John Palowitch
- Abstract summary: This work presents techniques for classifying linked content spread on forum websites using user interaction signals alone.
Online forums such as Reddit do not have a user-generated social graph, which is assumed in social network behavioral-based classification settings.
We show how to obtain a derived social graph, and use this graph, Reddit post sequences, and comment trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder.
- Score: 14.868643774881624
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The widespread adoption of online social networks in daily life has created a
pressing need for effectively classifying user-generated content. This work
presents techniques for classifying linked content spread on forum websites --
specifically, links to news articles or blogs -- using user interaction signals
alone. Importantly, online forums such as Reddit do not have a user-generated
social graph, which is assumed in social network behavioral-based
classification settings. Using Reddit as a case-study, we show how to obtain a
derived social graph, and use this graph, Reddit post sequences, and comment
trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder. We train
the R-GNN on news link categorization and rumor detection, showing superior
results to recent baselines. Our code is made publicly available at
https://github.com/google-research/social_cascades.
Related papers
- GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based
Histogram Intersection [51.608147732998994]
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning.
We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
arXiv Detail & Related papers (2024-01-17T13:04:23Z) - A Survey of Graph Neural Networks for Social Recommender Systems [38.79810157156386]
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions and the user-to-user social relations.
With the advance of graph neural networks (GNN), many GNN-based SocialRS methods have been developed recently.
We conduct a comprehensive and systematic review of the literature on GNN-based SocialRS methods.
arXiv Detail & Related papers (2022-12-08T18:54:15Z) - Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link
Prediction [23.545059901853815]
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graphstructured data.
We propose Neighborhood Overlap-aware Graph Neural Networks (Neo-GNNs) that learn useful structural features from an adjacency overlapped neighborhoods for link prediction.
arXiv Detail & Related papers (2022-06-09T01:43:49Z) - Graph-level Neural Networks: Current Progress and Future Directions [61.08696673768116]
Graph-level Neural Networks (GLNNs, deep learning-based graph-level learning methods) have been attractive due to their superiority in modeling high-dimensional data.
We propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
arXiv Detail & Related papers (2022-05-31T06:16:55Z) - Evidential Temporal-aware Graph-based Social Event Detection via
Dempster-Shafer Theory [76.4580340399321]
We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network.
We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively.
Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
arXiv Detail & Related papers (2022-05-24T16:22:40Z) - A comparative analysis of Graph Neural Networks and commonly used
machine learning algorithms on fake news detection [0.0]
Low cost, simple accessibility via social platforms, and a plethora of low-budget online news sources are some of the factors that contribute to the spread of false news.
Most of the existing fake news detection algorithms are solely focused on the news content only.
engaged users prior posts or social activities provide a wealth of information about their views on news and have significant ability to improve fake news identification.
arXiv Detail & Related papers (2022-03-26T18:40:03Z) - Harnessing the Power of Ego Network Layers for Link Prediction in Online
Social Networks [0.734084539365505]
Predictions are typically based on unsupervised or supervised learning.
We argue that richer information about personal social structure of individuals might lead to better predictions.
We show that social-awareness generally provides significant improvements in the prediction performance.
arXiv Detail & Related papers (2021-09-19T18:49:10Z) - Learning Graph Representations [0.0]
Graph Neural Networks (GNNs) are efficient ways to get insight into large dynamic graph datasets.
In this paper, we discuss the graph convolutional neural networks graph autoencoders and Social-temporal graph neural networks.
arXiv Detail & Related papers (2021-02-03T12:07:55Z) - Graph Neural Networks: Architectures, Stability and Transferability [176.3960927323358]
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs.
They are generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters.
arXiv Detail & Related papers (2020-08-04T18:57:36Z) - Graphon Neural Networks and the Transferability of Graph Neural Networks [125.71771240180654]
Graph neural networks (GNNs) rely on graph convolutions to extract local features from network data.
We introduce graphon NNs as limit objects of GNNs and prove a bound on the difference between the output of a GNN and its limit graphon-NN.
This result establishes a tradeoff between discriminability and transferability of GNNs.
arXiv Detail & Related papers (2020-06-05T16:41:08Z) - Stealing Links from Graph Neural Networks [72.85344230133248]
Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs)
Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection.
We propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph.
arXiv Detail & Related papers (2020-05-05T13:22:35Z)
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.