Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSybilFence: Improving Social-Graph-Based Sybil Defenses with User Negative Feedback
274
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Detecting and suspending fake accounts (Sybils) in online social networking (OSN) services protects both OSN operators and OSN users from illegal exploitation. Existing social-graph-based defense schemes effectively bound the accepted Sybils to the total number of social connections between Sybils and non-Sybil users. However, Sybils may still evade the defenses by soliciting many social connections to real users. We propose SybilFence, a system that improves over social-graph-based Sybil defenses to further thwart Sybils. SybilFence is based on the observation that even well-maintained fake accounts inevitably receive a significant number of user negative feedback, such as the rejections to their friend requests. Our key idea is to discount the social edges on users that have received negative feedback, thereby limiting the impact of Sybils social edges. The preliminary simulation results show that our proposal is more resilient to attacks where fake accounts continuously solicit social connections over time.
rate research
Read More
We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.
We introduce a new threshold model of social networks, in which the nodes influenced by their neighbours can adopt one out of several alternatives. We characterize social networks for which adoption of a product by the whole network is possible (respectively necessary) and the ones for which a unique outcome is guaranteed. These characterizations directly yield polynomial time algorithms that allow us to determine whether a given social network satisfies one of the above properties. We also study algorithmic questions for networks without unique outcomes. We show that the problem of determining whether a final network exists in which all nodes adopted some product is NP-complete. In turn, the problems of determining whether a given node adopts some (respectively, a given) product in some (respectively, all) network(s) are either co-NP complete or can be solved in polynomial time. Further, we show that the problem of computing the minimum possible spread of a product is NP-hard to approximate with an approximation ratio better than $Omega(n)$, in contrast to the maximum spread, which is efficiently computable. Finally, we clarify that some of the above problems can be solved in polynomial time when there are only two products.
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.
We deal with the problem of automatically generating social networks by analyzing and assessing smartphone usage and interaction data. We start by assigning weights to the different types of interactions such as messaging, email, phone calls, chat and physical proximity. Next, we propose a ranking algorithm which recognizes the pattern of interaction taking into account the changes in the collected data over time. Both algorithms are based on recent findings from social network research.
Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to pursue this issue, advancing knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individuals social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementation of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
