This paper introduces SARA, a semantic-assisted reinforced active learning framework for enhancing entity alignment (EA) under limited supervision scenarios. SARA addresses the challenges of EA in real-world scenarios, including knowledge graph heterogeneity and limited training ground truth. SARA effectively selects valuable entity pairs with limited labeled data by combining reinforced active learning and semantic information. It utilizes a pair-wise language model based on Sentence-BERT to learn informative name embeddings that capture entity name semantics. These embeddings are combined with structural embeddings and trained using a novel semantic-assisted alignment loss. Extensive experiments on benchmark datasets and a real-world dataset demonstrate the superiority of SARA over existing approaches, particularly in limited labeled data scenarios. The paper also provides insights into fine-tuning strategies, presents ablation studies, and conducts sensitivity analyses to validate the effectiveness of SARA.

In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. RecDelta is a web-based information system where people visually compare the performance of various recommendation algorithms and their recommended items. In the proposed system, we visualize the distribution of the δ scores between algorithms--a distance metric measuring the intersection between recommendation lists. Such visualization allows for rapid identification of users for whom the items recommended by different algorithms diverge or vice versa; then, one can further select the desired user to present the relationship between recommended items and his/her historical behavior. RecDelta benefits both academics and practitioners by enhancing model explainability as they develop recommendation algorithms with their newly gained insights. Note that while the system is now online at https://cfda.csie.org/recdelta, we also provide a video recording at https://tinyurl.com/RecDelta to introduce the concept and the usage of our system.

Considering the temporal order of user-item interactions for recommendation forms a novel class of recommendation algorithms in recent years, among which sequential recommendation models are the most popular approaches. Although, theoretically, such fine-grained modeling should be beneficial to the recommendation performance, these sequential models in practice greatly suffer from the issue of data sparsity as there are a huge number of combinations for item sequences. To address the issue, we propose LSTPR, a graph-based matrix factorization model that incorporates both high-order graph information and long short-term user preferences into the modeling process. LSTPR explicitly distinguishes long-term and short-term user preferences and enriches the sparse interactions via random surfing on the user-item graph. Experiments on three recommendation datasets with temporal user-item information demonstrate that the proposed LSTPR model achieves significantly better performance than the seven baseline methods.

This work tries to bring a marriage between two areas of computer science, social network analysis and machine learning, by exploiting ranking-based learning models for preference prediction on social networks. In the field of social network analysis, the diffusion of information on social networks has been studied for decades. This paper proposes the study of diffusion of preference on social networks. In general, there are two types of approaches proposed to predict the diffusion of information on a network, model-driven and data-driven approaches. The former assumes an underlying mechanism for diffusion while the latter tries to learn a more flexible model with the given data. This paper first proposes a simple modification on the existing model-driven binary diffusion approaches for preference list diffusion, and then addresses some concerns by proposing a rank-learning based data-driven approach. To evaluate the approaches, we propose two scenarios which data can be obtained from publicly available sources, namely predicting the preference propagation about the citation behavior and the microblogging behavior. The experiments show that the proposed ranking-based data-driven method outperforms all the other competitors significantly in both evaluation scenarios.

Issues about information diffusion on social networks has been studied for decades. To simplify the analysis, most models consider the propagated information or media as single real values. Representing media as single values however would not suitable for certain situations such as the voter preference toward the candidates in an election. In such case, the representation would better be lists instead of single values as people sometimes can alter others’ preference through toward objects social inference. This paper studies the diffusion of preference on social networks, which is a novel problem to solve in this direction. First, we propose a preference propagation model that can handle the diffusion of vector-type information instead of only binary or numerical values. Furthermore, we theoretically prove the convergence of diffusion with the proposed model, and that a consensus among strongly connected nodes can eventually be reached with certain conditions. We further extract relevant information from a publicly available bibliography datasets to evaluate the proposed models, while such data can further serve as a benchmark for evaluating future models of the same purpose. Lastly, we exploit the extracted data to demonstrate the usefulness of our model and compare it with other well-known diffusion strategies such as independent cascade, linear threshold, and diffusion rank. We find that our model consistently outperforms other models.

Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.

The vertex-betweenness centrality index is an essential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V| |E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V|) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks.

For current Web 2.0 services, manual examination of user uploaded content is normally required to ensure its legitimacy and appropriateness, which is a substantial burden to service providers. To reduce labor costs and the delays caused by content censoring, social moderation has been proposed as a front-line mechanism, whereby user moderators are encouraged to examine content before system moderation is required. Given the immerse amount of new content added to the Web each day, there is a need for automation schemes to facilitate rear system moderation. This kind of mechanism is expected to automatically summarize reports from user moderators and ban misbehaving users or remove inappropriate content whenever possible. However, the accuracy of such schemes may be reduced by collusion attacks, where some work together to mislead the automatic summarization in order to obtain shared benefits. In this paper, we propose a collusion-resistant automation scheme for social moderation systems. Because some user moderators may collude and dishonestly claim that a user misbehaves, our scheme detects whether an accusation from a user moderator is fair or malicious based on the structure of mutual accusations of all users in the system. Through simulations we show that collusion attacks are likely to succeed if an intuitive count- based automation scheme is used. The proposed scheme, which is based on the community structure of the user accusation graph, achieves a decent performance in most scenarios.

The demand for effective VoIP and online gaming traffic management methods continues to increase for purposes such as QoS provisioning, usage accounting, and blocking VoIP calls or game connections. However, identifying such flows has become a significant administrative burden because many of the applications use proprietary signaling and transport protocols. The question of how to identify proprietary VoIP traffic has yet to be solved. In this paper, we propose using a deviation-based classifier to identify VoIP and gaming traffic, given that such real-time interactive services normally send out constant-packet-rate (CPR) traffic with a fixed interval, in order to maintain real-timeliness and interactivity. Our contribution is two-fold: 1) We show that scale-free variability measures are more appropriate than scale- dependent ones for quantifying the network variability injected into CPR traffic. 2) Our proposed classifier is particularly lightweight in that it only requires a few inter-packet times to make a decision. The evaluation results show that by only analyzing 10 successive inter-packet times, we can distinguish between CPR and non-CPR traffic with approximately 90% accuracy.

Peer-to-peer relaying is commonly used in real-time applications to cope with NAT and firewall restrictions and provide better quality network paths. As relaying is not natively supported by the Internet, it is usually implemented at the application layer. Also, in a modern operating system, the processor is shared, so the receive-process-forward process for each relay packet may take a considerable amount of time if the host is busy handling some other tasks. Thus, if we happen to select a loaded relay node, the relaying may introduce significant delays to the packet transmission time and even degrade the application performance. In this work, based on an extensive set of Internet traces, we pursue an understanding of the processing delays incurred at relay nodes and their impact on the application performance. Our contribution is three-fold: 1) we propose a methodology for measuring the processing delays at any relay node on the Internet; 2) we characterize the workload patterns of a variety of Internet relay nodes; and 3) we show that, serious VoIP quality degradation may occur due to relay processing, thus we have to monitor the processing delays of a relay node continuously to prevent the application performance from being degraded.