DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

The fast growth of social networks and their data access limitations in recent years has led to increasing difficulty in obtaining the complete topology of these networks. However, diffusion information over these networks is available, and many algorithms have been proposed to infer the underlying networks using this information. The previously proposed algorithms only focus on inferring more links and ignore preserving the critical topological characteristics of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. It is based on the Markov transition matrix derived from time series cascades, as well as the node-node similarity that can be observed in the cascade behavior from a structural point of view. In addition, the presented method has linear time complexity (increases linearly with the number of nodes, number of cascades, and square of the average length of cascades), and its distributed version in the MapReduce framework is also scalable. We applied the proposed approach to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time while maintaining structural properties, including modular structure, degree distribution, connected components, density, and clustering coefficients, than well-known network inference methods.

FNR: A Similarity and Transformer-Based Approachto Detect Multi-Modal FakeNews in Social Media

The availability and interactive nature of social media have made them the primary source of news around the globe. The popularity of social media tempts criminals to pursue their immoral intentions by producing and disseminating fake news using seductive text and misleading images. Therefore, verifying social media news and spotting fakes is crucial. This work aims to analyze multi-modal features from texts and images in social media for detecting fake news. We propose a Fake News Revealer (FNR) method that utilizes transform learning to extract contextual and semantic features and contrastive loss to determine the similarity between image and text. We applied FNR on two real social media datasets. The results show the proposed method achieves higher accuracies in detecting fake news compared to the previous works.

Joint Inference of Structure and Diffusion in Partially Observed Social Networks

Access to complete data in large scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks do not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. The present paper tries to infer the unobserved data from both diffusion network and network structure by learning a model from the partially observed data. We develop a probabilistic generative model called "DiffStru" to jointly discover the hidden links of network structure and the omitted diffusion activities. The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled low dimensional latent factors. In addition to inferring the unseen data, the learned latent factors may also help network classification problems such as community detection. Simulation results on synthetic and real-world datasets show the excellent performance of the proposed method in terms of link prediction and discovering the identity and infection time of invisible social behaviors.

News Labeling as Early as Possible: Real or Fake?

Making disguise between real and fake news propagation through online social networks is an important issue in many applications. The time gap between the news release time and detection of its label is a significant step towards broadcasting the real information and avoiding the fake. Therefore, one of the challenging tasks in this area is to identify fake and real news in early stages of propagation. However, there is a trade-off between minimizing the time gap and maximizing accuracy. Despite recent efforts in detection of fake news, there has been no significant work that explicitly incorporates early detection in its model. In this paper, we focus on accurate early labeling of news, and propose a model by considering earliness both in modeling and prediction. The proposed method utilizes recurrent neural networks with a novel loss function, and a new stopping rule. Given the context of news, we first embed it with a class-specific text representation. Then, we utilize the available public profile of users, and speed of news diffusion, for early labeling of the news. Experiments on real datasets demonstrate the effectiveness of our model both in terms of early labelling and accuracy, compared to the state of the art baseline and models.