Abstract: With the popularity of the Internet, information on the web is spreading to the public at an astonishing speed. However, false information and rumors are also rapidly spreading due to the cascade effect, causing great harm to society. Finding the source of rumor spread on social networks plays a crucial role in suppressing the spread of rumors. Most of the traditional rumor source localization methods fail to integrate multi-source features and the accuracy of localization still needs to be further improved. Therefore, this paper proposes a deep learning-based rumor source localization method that identifies the rumor source based on multi-source features observed in nodes affected by rumors. This method first obtains the influence vectors of nodes based on the similarity of influence between the node and observed nodes. Then, it uses autoencoder networks to encode the influence vectors of nodes, obtaining new embedding representations that contain node information, diffusion paths, and propagation time information. Finally, it calculates the probability of nodes being the source based on their new influence vectors to locate the rumor source. Experimental results on two simulated datasets and four real datasets show that compared with other methods, the proposed method can locate the rumor source at a faster speed and improve the accuracy of rumor source localization by more than 25%. 

Key words: social network, node representation, rumor source, multi-rumor source localization