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Tian Bian

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4 papers

2 author rows

EAAI Journal 2025 Journal Article

A novel high-accuracy graph neural network-based rumor detection method

Xi Xiao
Zeming Wu
Chengzong Cai
Tian Bian
Guangwu Hu
Qing Li
Cheng Huang

Rumors spreading on social media platforms result in potential damages. A precise rumor detection mechanism can help form a healthy public opinion environment. In recent years, deep learning-based rumor detection methods, especially graph model-based ones, have risen and reached promising performance. However, there are several defects in existing methods, which limit models from efficiently utilizing the propagation structure. In this paper, we propose a novel rumor detection model, which has high accuracy and reaches state-of-the-art performance. First, we design a powerful comprehensive rumor feature extractor that explicitly overcomes the restriction of previous Graph Neural Networks-based models. Then, by introducing Kernel Subtree features, our model acquires the capability to learn crucial local features from important nodes. Comparative experiments performed on two real-world social media platforms demonstrate that our work reaches state-of-the-art performance, which outperforms the best baseline with 1. 6% and 1. 9% in accuracy respectively.

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ICML Conference 2025 Conference Paper

IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck

Tian Bian
Yifan Niu
Chaohao Yuan
Chengzhi Piao
Bingzhe Wu
Long-Kai Huang
Yu Rong 0001
Tingyang Xu

Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subgraphs, also known as circuits, within the model that are responsible for solving specific tasks. However, most existing studies overlook the holistic nature of these circuits and require designing specific corrupted activations for different tasks, which is inaccurate and inefficient. In this work, we propose an end-to-end approach based on the principle of Information Bottleneck, called IBCircuit, to holistically identify informative circuits. In contrast to traditional causal interventions, IBCircuit is an optimization framework for holistic circuit discovery and can be applied to any given task without tediously corrupted activation design. In both the Indirect Object Identification (IOI) and Greater-Than tasks, IBCircuit identifies more faithful and minimal circuits in terms of critical node components and edge components compared to recent related work.

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TMLR Journal 2023 Journal Article

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

Xuefeng Du
Tian Bian
Yu Rong
Bo Han
Tongliang Liu
Tingyang Xu
Wenbing Huang
Yixuan Li

Teaching Graph Neural Networks (GNNs) to accurately classify nodes under severely noisy labels is an important problem in real-world graph learning applications, but is currently underexplored. Although pairwise training methods have demonstrated promise in supervised metric learning and unsupervised contrastive learning, they remain less studied on noisy graphs, where the structural pairwise interactions (PI) between nodes are abundant and thus might benefit label noise learning rather than the pointwise methods. This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs, which relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels. Our proposed framework PI-GNN contributes two novel components: (1) a confidence-aware PI estimation model that adaptively estimates the PI labels, which are defined as whether the two nodes share the same node labels, and (2) a decoupled training approach that leverages the estimated PI labels to regularize a node classification model for robust node classification. Extensive experiments on different datasets and GNN architectures demonstrate the effectiveness of PI-GNN, yielding a promising improvement over the state-of-the-art methods. Code is publicly available at https://github.com/TianBian95/pi-gnn.

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AAAI Conference 2020 Conference Paper

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Tian Bian
Xi Xiao
Tingyang Xu
Peilin Zhao
Wenbing Huang
Yu Rong
Junzhou Huang

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation; and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from source post is involved in each layer of GCN to enhance the inﬂuences from the roots of rumors. Encouraging empirical results on several benchmarks conﬁrm the superiority of the proposed method over the state-of-the-art approaches.

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