Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose GCNEXT (Graph Convolutional Network with Expended Balance Theory), an end-to-end framework based on graph convolutional networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. The experimental results on seven real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
, Spatial incoherence of an earthquake ground motion may result in reduction in high-frequency components of the input motion for a building or a structure. Two-dimensional finite element method is used for heterogeneous soil medium. Through Monte Carlo simulation, the spatially correlated soil model shows more reducing effect in the average input with increasing frequency and with increasing correlation distance. Such effect is caused by the scattering damping and depends on the wavelength, the correlation distance, the traveling distance and the deviation of the soil properties.
Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose an end-to-end framework based on Graph Convolutional Networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. Experimental results on four real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks, that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
With the spread of social media and e-commerce websites, the technology of user profiling from users' action history has attracted a lot of interest. Users' action history can be acquired both in the passive way and in the active (interactive) way, and previous studies have found out how to presume the users' profile from their action history which is acquired passively. The purpose of this study was to find out the best way to interact with users for efficient user profiling. First, we constructed a CNN (Convolutional Neural Network) model which presumes user's profile. Next, we proposed three ways to interact with users for efficient user profiling and compared them. Consequently, two ways of the three were discovered to be effective to streamline the user profiling. And we provided some analyses of the ways, which revealed that the CNN model's output for an item could be utilized for efficient user profiling , and considering the probability that each item is consumed by the target user could provide more efficient ways.
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