Finding Spread Blockers in Dynamic Networks

A survey on rumor detection and prevention in social media using deep learning

In the current digital era, massive amounts of unreliable, purposefully misleading material, such as texts and images, are being shared widely on various web platforms to deceive the reader. Most of us use social media sites to exchange or obtain information. This opens a lot of space for false information, like fake news, rumors, etc., to spread that could harm a society's social fabric, a person's reputation, or the legitimacy of a whole country. Therefore, preventing the transmission of such dangerous material across platforms is a digital priority. However, the main goal of this survey paper is to thoroughly examine several current state-of-the-art research works on rumor control (detection and prevention) that use deep learning-based techniques and to identify major distinctions between these research efforts. The comparison results are intended to identify research gaps and challenges for rumor detection, tracking, and combating. This survey of the literature makes a significant contribution by highlighting several cutting-edge deep learning-based models for rumor detection in social media and critically evaluating their effectiveness on recently available standard datasets. Furthermore, to have a thorough grasp of rumor prevention to spread, we also looked into various pertinent approaches, including rumor veracity classification, stance classification, tracking, and combating. We also have created a summary of recent datasets with all the necessary information and analysis. Finally, as part of this survey, we have identified some of the potential research gaps and challenges that need to be addressed in order to develop early, effective methods of rumor control.

Rumors clarification with minimum credibility in social networks

In 2020, the information about Corona Virus Disease 2019 (COVID-19) is overwhelming, which is mixed with a lot of rumors. Rumor and truth can change people's believes more than once, depending on who is more credible. Here we use credibility to measure the influence one person has on others. Considering costs, we often hope to find the people with the smallest credibility but can achieve the maximum influence. Therefore, we focus on how to use minimal credibility in a given amount of time to clarify rumors. Given the time t , the minimum credibility rumor clarifying ( M C R C ) problem aims to find a seed set with k users such that the total credibility can be minimized when the total number of the users influenced by positive information reaches a given number at time t . In this paper, we propose a Longest-Effective-Hops algorithm called LEH to solve this problem that supposes each user can be influenced two or more times. The theoretical analysis proves that our algorithm is universal and effective. Extensive contrast experiments show that our algorithm is more efficient in both time and performance than the state-of-the art methods.

Benchmarking Measures of Network Influence

Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.

Assessing the Influence of an Individual Event in Complex Fault Spreading Network Based on Dynamic Uncertain Causality Graph

Identifying the pivotal causes and highly influential spreaders in fault propagation processes is crucial for improving the maintenance decision making for complex systems under abnormal and emergency situations. A dynamic uncertain causality graph-based method is introduced in this paper to explicitly model the uncertain causalities among system components, identify fault conditions, locate the fault origins, and predict the spreading tendency by means of probabilistic reasoning. A new algorithm is proposed to assess the impacts of an individual event by investigating the corresponding node's time-variant betweenness centrality and the strength of global causal influence in the fault propagation network. The algorithm does not depend on the whole original and static network but on the real-time spreading behaviors and dynamics, which makes the algorithm to be specifically targeted and more efficient. Experiments on both simulated networks and real-world systems demonstrate the accuracy, effectiveness, and comprehensibility of the proposed method for the fault management of power grids and other complex networked systems.