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Graph Neural Networks for Social Recommendation Open
In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to a…
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Session-Based Social Recommendation via Dynamic Graph Attention Networks Open
Online communities such as Facebook and Twitter are enormously popular and\nhave become an essential part of the daily life of many of their users. Through\nthese platforms, users can discover and create information that others will\nthen …
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Federated Social Recommendation with Graph Neural Network Open
Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerf…
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Rumor Detection on Social Media with Graph Structured Adversarial Learning Open
The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. Howeve…
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Sybil Defense Techniques in Online Social Networks: A Survey Open
The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online. For example, this problem can affect …
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Compositional Fairness Constraints for Graph Embeddings Open
Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fai…
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Deep Graph Representation Learning and Optimization for Influence Maximization Open
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progress in designing various traditional methods, and the…
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A Graph-Based Socioeconomic Analysis of Steemit Open
Online social networks (OSNs) have changed the way of how people interact; however, lately, people are questioning more and more their business models. During the last ten years, new solutions based on decentralized architectures have been…
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An Efficient and Effective Framework for Session-based Social Recommendation Open
In many applications of session-based recommendation, social networks are usually available. Since users' interests are influenced by their friends, recommender systems can leverage social networks to better understand their users' prefere…
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Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction Open
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. Pedestrian trajectories are not only influenced by the pedestrian itself but also …
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Graph Neural Networks for Social Recommendation Open
In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to a…
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The YouTube Social Network Open
Today, YouTube is the largest user-driven video content provider in the world; it has become a major platform for disseminating multimedia information. A major contribution to its success comes from the user-to-user social experience that …
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Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network Open
Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can a…
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A Survey on the Recent Advances of Deep Community Detection Open
In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteris…
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DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation Open
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied …
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SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce Open
Interactive Recommendation (IR) formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals' feedback in multiple steps and optimize the long-term user benefit of recommendation. Deep Re…
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Improving Cyberbullying Detection with User Interaction Open
Cyberbullying, identified as intended and repeated online bullying behavior,\nhas become increasingly prevalent in the past few decades. Despite the\nsignificant progress made thus far, the focus of most existing work on\ncyberbullying det…
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In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale Open
How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting …
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Beyond $1/2$-Approximation for Submodular Maximization on Massive Data Streams Open
Many tasks in machine learning and data mining, such as data diversification, non-parametric learning, kernel machines, clustering etc., require extracting a small but representative summary from a massive dataset. Often, such problems can…
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ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks Open
Given a large graph with millions of vertices and different types, how can we spot anomalies and find potential adversaries in time? Most graph-based fraud detection algorithms focus on finding dense blocks, discovering local subgraphs, de…
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DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection Open
Recent efforts in fake news detection have witnessed a surge of interest in\nusing graph neural networks (GNNs) to exploit rich social context. Existing\nstudies generally leverage fixed graph structures, assuming that the graphs\naccurate…
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Exploiting Temporal Dynamics in Sybil Defenses Open
Sybil attacks present a significant threat to many Internet systems and applications, in which a single adversary inserts multiple colluding identities in the system to compromise its security and privacy. Recent work has advocated the use…
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A Survey on the Use of Graph Convolutional Networks for Combating Fake News Open
The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detectio…
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Efficient Algorithms for Public-Private Social Networks Open
We introduce the public-private model of graphs. In this model, we have a public graph and each node in the public graph has an associated private graph. The motivation for studying this model stems from social networks, where the nodes ar…
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Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach Open
Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new ap…
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Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks Open
Research on influence maximization ofter has to cope with marketing needs relating to the propagation of information towards specific users. However, little attention has been paid to the fact that the success of an information diffusion c…
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SmartWalk Open
Random walks form a critical foundation in many social network based security systems and applications. Currently, the design of such social security mechanisms is limited to the classical paradigm of using fixed-length random walks for al…
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In a World That Counts Open
How can web services that depend on user generated content discern fake\nsocial engagement activities by spammers from legitimate ones? In this paper,\nwe focus on the social site of YouTube and the problem of identifying bad\nactors posti…
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An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks Open
With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existin…
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MET: A Fast Algorithm for Minimizing Propagation in Large Graphs with Small Eigen-Gaps Open
Given the topology of a graph G and a budget k, how can we quickly find the best k edges to delete that minimize dissemination in G? Stopping dissemination in a graph is important in a variety of fields from epidemiology to cyber security.…