FGDGNN : Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025)

Abstract: Detecting rumors on social media has become a crucial issue.Propagation structure-based methods have recently attracted increasing attention.When the propagation structure is represented by the dynamic graph, temporal information is considered.However, existing rumor detection models using dynamic graph typically focus only on coarse-grained temporal information and ignore the fine-grained temporal dynamics within individual snapshots and across snapshots.In this paper, we propose a novel Fine-Grained Dynamic Graph Neural Network (FGDGNN) model, which can incorporate the fine-grained temporal information of dynamic propagation graph in the intra-snapshot and dynamic embedding update mechanism in the inter-snapshots into a unified framework for rumor detection.Specifically, we first construct the edge-weighted propagation graph and the edge-aware graph isomorphism network is proposed.To obtain fine-grained temporal representations across snapshots, we propose an embedding transformation layer to update node embeddings.Finally, we integrate the temporal information in the inter-snapshots at the graph level to enhance the effectiveness of the proposed model.Extensive experiments conducted on three public real-world datasets demonstrate that our FGDGNN model achieves significant improvements compared with the state-of-the-art baselines.

## introduction social media has become the major platform for information sharing among the public. however, the proliferation of social media also brings significant challenges. one of the main challenges is the rapid spread of rumors, which can pose severe risks to public trust, people's health, and social stability. therefore, it has become increasingly important to develop effective methods for identifying and combating rumors. previous research relies on manually designed features and machine learning classifiers to identify rumors @xcite @xcite . to overcome the limitations of handcrafted features, deep learning models such as recurrent neural networks (rnns) and convolutional neural networks (cnns) have been used for rumor detection to automatically extract high-level representations from the content-based methods and propagation structure-based methods @xcite @xcite . the propagation structure-based methods, which have achieved superior detection performance, have attracted more and more attention in recent years @xcite @xcite . however, existing propagation structure-based methods usually consider the static graph structure of the final state of rumor propagation, and ignore the temporal dynamics of rumor propagation. figure 1 (a) illustrates a static graph structure of rumor propagation. the temporal features of propagation refer to the order and interval of replied or retweeted posts along the timeline, as reflected in the timestamps of user engagements. therefore, some studies @xcite @xcite @xcite have explored the temporal dynamics of news events and proposed dynamic graphs to model the spread of rumors on social media. a dynamic graph of event propagation is shown in figure 1 @xcite . these methods, which are usually built using graph neural networks (gnns), emphasize the transformation and aggregation of graph features, but fail to capture detailed temporal features of propagation, such as the speed, depth and breadth that characterize the propagation of a rumor event. to address this issue, dynamic propagation graph composed of a series of snapshots have been constructed to model temporal dynamics @xcite @xcite . these works treat snapshots of dynamic graphs as isolated from one another or only allow for coarse-grained interactions between two consecutive snapshots (i.e. inter-snapshots). coarsegrained information often neglects edge-level variations, treating all connections equally. it fails to capture key rumor-spreading patterns, such as sudden bursts of interactions or gradual spread over time. moreover, the coarse-grained information used only in the overall graph representation often ignores the impact of node-level variations and the interactions among different snapshots within an event. this leads to a weak structural representation. in contrast, fine-grained interactions aim to capture detailed temporal variations at both the edge and node levels, leveraging more comprehensive information to enhance the model's performance. therefore, fine-grained temporal features are required to capture the details of propagation. we divide the temporal granularity of propagation into the edge-aware and node-level granularity. the edge-weighted propagation graph, which can represent the speed, depth and breadth of propagation, is used to describe edge-aware granularity in the intrasnapshot. as shown in figure 1 (b), the weighted graph is used to represent the propagation graph, where the weight on each edge indicates the time interval between the creation of a post and the creation of its response. the node-level granularity is adopted to capture the temporal dynamics in the inter-snapshots. in this paper, we propose a novel fine-grained dynamic graph neural network (fgdgnn) model, which incorporates the edge-aware temporal information of dynamic propagation graph in the intra-snapshot and the node-level dynamic up-date in the inter-snapshots into a unified framework for rumor detection. specifically, we first construct an edge-weighted propagation graph, in which time intervals are used as edge weights. the propagation process is represented as a sequence of graph snapshots. then, we propose an edge-aware graph isomorphism network (eagin) to make full use of edge weights to capture detailed temporal features in the intra-snapshot. to obtain fine-grained temporal representations in the inter-snapshots, we propose an embedding transformation layer to update node embeddings. finally, we integrate the temporal information in the inter-snapshots at the graph level with the framework to enhance the effectiveness of the proposed fgdgnn model. the main contributions of this paper can be summarized as follows: ## conclusion in this paper, we propose a novel fine-grained dynamic graph neural network (fgdgnn) model for rumor detection. we construct the edgeweighted propagation graph in which the time intervals are used as edge weights each snapshot. additionally, we propose an embedding transformation layer to update node embeddings across snapshots. experiments on three public datasets demonstrate that the fgdgnn model outperforms the state-of-the-art baselines.