Jing-Kai Lou

Analysis of revisitations in online games

This paper analyzes revisitations in online games focusing on two types of revisitations: game revisitations and area revisitations. A player revisits a game and areas therein with purposes. For game revisitations, we conduct a large-scale analysis using Shen Zhou Online access log collected for nearly 6 years consisting of 50,000 characters and have succeeded in using in the information on game revisitations, together with the login time and login frequency information, for predicting the players who will be absent from the game. For area revisitations, we conduct yet another large-scale analysis using World of Warcraft access log collected for 2 years consisting of more than 60,000 characters and have discovered four main groups of area revisitation patterns. We also discuss in the paper how our findings can be utilized to support both game developers and players.

A Collusion-Resistant Automation Scheme for Social Moderation Systems

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.

What Can the Temporal Social Behavior Tell Us? An Estimation of Vertex-Betweenness Using Dynamic Social Information

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(b^2|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.

Rapid Detection of Constant-Packet-Rate Flows

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.

Fairness-Aware Loan Recommendation for Microfinance Services

Up to date, more than 15 billion US dollars have been invested in microfinance that benefited more than 160 million people in developing countries. The Kiva organization is one of the successful examples that use a decentralized matching process to match lenders and borrowers. Interested lenders from around the world can look for cases among thousands of applicants they found promising to lend the money to. But how can loan borrowers and lenders be successfully matched up in a microfinance platform like Kiva? We argue that a sophisticate recommender not only pairs up loan lenders and borrowers in accordance to their preferences, but should also help to diversify the distribution of donations to reduce the inequality of loans is highly demanded, as altruism, like any resource, can be congestible. In this paper, we propose a fairness-aware recommendation system based on one-class collaborative-filtering techniques for charity and micro-loan platform such as Kiva.org. Our experiments on real dataset indicates that the proposed method can largely improve the loan distribution fairness while retaining the accuracy of recommendations.

Toward an understanding of the processing delay of peer-to-peer relay nodes

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.

Exploiting rank-learning models to predict the diffusion of preferences on social networks

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.

Analysis of Area Revisitation Patterns in World of Warcarft

This paper analyzes area revisitation patterns in World of Warcraft (WoW). Online-game players roam a number of in-game areas while playing the game and revisit some of them with different personal reasons. To clarify this issue, we conduct a large-scale analysis using WoW access log collected for two years consisting of more than sixty thousand characters and have discovered four main groups of area revisitation patterns. We describe also in the paper how our findings can be utilized to support both game developers and players.

A Social Diffusion Model with an Application on Election Simulation

Issues about opinion diffusion have been studied for decades. It has so far no empirical approach to model the interflow and formation of crowds opinion in elections due to two reasons. First, unlike the spread of information or flu, individuals have their intrinsic attitudes to election candidates in advance. Second, opinions are generally simply assumed as single values in most diffusion models. However, in this case, an opinion should represent preference toward multiple candidates. Previously done models thus may not intuitively interpret such scenario. This work is to design a diffusion model which is capable of managing the aforementioned scenario. To demonstrate the usefulness of our model, we simulate the diffusion on the network built based on a publicly available bibliography dataset. We compare the proposed model with other well-known models such as independent cascade. It turns out that our model consistently outperforms other models. We additionally investigate electoral issues with our model simulator.