Journal Papers by Jaideep Srivastava
Sleep is an important human behavior with a deep impact on quality of life. Inadequate sleep qual... more Sleep is an important human behavior with a deep impact on quality of life. Inadequate sleep quality negatively affects both mental and physical well-being, and exacerbates many health problems such as diabetes, depression, cancer and obesity. Alarmingly, poor sleep is becoming a growing concern in our society. Increased efforts toward the development of sleep science, focus on the study of sleep and its impact on overall human health. Technology has become a crucial component for this research, particularly the use of wearable devices for capturing and analysing human activity. In this article, we explore the use of wearables in sleep science, including the algorithmic development of robust human activity recognition and predictive methodologies for sleep quality estimation, which have shown an AUC improvement up to twofold over current actigraphy methods by 50%. These technologies are crucial for the evolution of new application services to assist behavioural health decision-making for patients and professionals.
Papers by Jaideep Srivastava
ABSTRACT Understanding the dynamics of reciprocation is of great interest in sociology and comput... more ABSTRACT Understanding the dynamics of reciprocation is of great interest in sociology and computational social science. The recent growth of Massively Multi-player Online Games (MMOGs) has provided unprecedented access to large-scale data which enables us to study such complex human behavior in a more systematic manner. In this paper, we consider three different networks in the EverQuest2 game: chat, trade, and trust. The chat network has the highest level of reciprocation (33%) because there are essentially no barriers to it. The trade network has a lower rate of reciprocation (27%) because it has the obvious barrier of requiring more goods or money for exchange; morever, there is no clear benefit to returning a trade link except in terms of social connections. The trust network has the lowest reciprocation (14%) because this equates to sharing certain within-game assets such as weapons, and so there is a high barrier for such connections because they require faith in the players that are granted such high access. In general, we observe that reciprocation rate is inversely related to the barrier level in these networks. We also note that reciprocation has connections across the heterogeneous networks. Our experiments indicate that players make use of the medium-barrier reciprocations to strengthen a relationship. We hypothesize that lower-barrier interactions are an important component to predicting higher-barrier ones. We verify our hypothesis using predictive models for trust reciprocations using features from trade interactions. Using the number of trades (both before and after the initial trust link) boosts our ability to predict if the trust will be reciprocated up to 11% with respect to the AUC.
ABSTRACT The problem of finding the influencers in social networks has been traditionally dealt i... more ABSTRACT The problem of finding the influencers in social networks has been traditionally dealt in an optimization setting by finding the top-k nodes that has the maximum information spread in the network. These methods aim to find the influencers in a network through the process of information diffusion. However, none of these approaches model the individual social value generated by collaborations in these networks. Such social value is often the real motivation for which the nodes connect to each other. In this work, we propose a framework to compute this network social value using the concept of social capital, namely the amount of bonding and bridging connections in the network. We first compute the social capital value of the network and then allocate this network value to the nodes of the network. We establish the fairness of our allocation using several axioms of fairness. Our experiments on the real data sets show that the computed social capital is an excellent proxy for finding influencers and our approach outperforms several popular baselines.
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02, 2002
Special interest tracks and posters of the 14th international conference on World Wide Web - WWW '05, 2005
Link Analysis has been a popular and widely used Web mining technique, especially in the area of ... more Link Analysis has been a popular and widely used Web mining technique, especially in the area of Web search. Various ranking schemes based on link analysis have been proposed, of which the PageRank metric has gained the most popularity with the success of Google. Over the last few years, there has been significant work in improving the relevance model of
Lecture Notes in Computer Science, 2000
Proceedings of the 6th international conference on Web engineering - ICWE '06, 2006
Proceedings of the 9th ACM conference on Electronic commerce - EC '08, 2008
National Conference on Artificial Intelligence, 2010
Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14, 2014
ABSTRACT The last decade has been characterized by an explosion of social media in a variety of f... more ABSTRACT The last decade has been characterized by an explosion of social media in a variety of forms. Since the data is captured in digital form it has become possible for the first time study human behavior at a massive scale. Not only is it possible to address traditional questions in the social sciences regarding collective dynamics of human behaviors but it is also possible to study new types of human behaviors which have arisen as a result of usage of new mediums like twitter, YouTube, Facebook, one games etc. Each of these mediums has its respective limitations and affordances. Out of all these mediums the most complex and data rich medium is that of Massive Online Games (MOGs). MOGs refer to massive online persistent environments (World of Warcraft, EVE Online, EverQuest etc) shared by millions of people . In general these environments are characterized by a rich array of activities and social interactions with a wide array of behaviors e.g., cooperation, trade, quest, deceit, mentoring etc. Such environments allow one to study human behavior at a level of granularity where it was not possible to do so previously. Given the challenges associated with analyzing this type of data traditional techniques in data mining and social network analysis have to be extended with insights from the social sciences. The tutorial will cover predictive and generative models in the study of MOGs. Additionally we will cover some SNA techniques which are more appropriate for MOGs given the multi-dimensionality of the data (P*/ERGM Models, IR Based Network Analysis, Hypergrah based Techniques, Coextensive Social Networks etc). We also describe the various ways in which MOGs exhibit similarities to the real world e.g., economic behaviors, clandestine behaviors, mentoring etc).
2010 IEEE Second International Conference on Social Computing, 2010
Proceedings of the 1999 International Conference on Parallel Processing, 1999
2013 IEEE 13th International Conference on Data Mining Workshops, 2013
ABSTRACT In this paper, we study the problem of anomaly detection with application to aviation sy... more ABSTRACT In this paper, we study the problem of anomaly detection with application to aviation systems. We proposed a framework for detecting precursors to aviation safety incidents due to human factors based on Hidden Semi-Markov Models (HSMM). We investigate HSMMs due to their inherent ability to model durations in addition to model latent state transitions based on the observed pilots actions. Empirical evaluation on synthetic data and flight simulator data show that HSMMs perform favorably compared to many other existing anomaly detection algorithms.
2010 IEEE International Conference on Data Mining Workshops, 2010
This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for ... more This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following key components: 1) sequence alignment-based player profile databases, 2) feature selection schemes and prediction model building, and 3) decision support model for determining inactive players.
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09, 2009
Over the past several years, there has been a great interest in topic detection and tracking (TDT... more Over the past several years, there has been a great interest in topic detection and tracking (TDT). Recently, analyzing general research trend from the huge amount of history documents also arouses considerable attention. However, existing work on TDT mainly focuses on overall trend analysis, and is unable to address questions such as "what determines the evolution of a topic?" and "when and how does a new topic get formed?". In this paper, we propose a core group model to explain the dynamics and further segment topic development. According to the division phase and interphase in the life cycle of a core group, a topic is separated into four states, i.e. birth state, extending state, saturation state and shrinkage state. Experimental results on a real dataset show that the division of a core group brings on the generation of a new topic, and the progress of an entire topic is closely correlated to the growth of a core group during its interphase.
2011 International Conference on Advances in Social Networks Analysis and Mining, 2011
Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and ha... more Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in EverQuest II, a
2011 International Conference on Advances in Social Networks Analysis and Mining, 2011
ABSTRACT This study investigates and reports preliminary findings on player performance predictio... more ABSTRACT This study investigates and reports preliminary findings on player performance prediction approaches which model player's past performance and social diversity in mentoring network in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. Our contributions include a better understanding of performance metrics used in the game and a foundation of recommendation systems for mentors and apprentices. We examined three different game servers from the EverQuest II game logs. In all three servers, the results from our analyses suggest that increase in social diversity in terms of characters and classes encountered moderately negatively correlates with player performance. Based on this finding, we built predictive models to predict player's future performance based on past performance and social diversity in terms of mentoring activities. Our results indicate that 1) models employing past performance and social diversity perform better and 2) prediction for mentors is generally better than that for apprentices. systems which can model not only player's past performance but social interactions which can influence player performance. In this study, we focus on statistical analysis of player performance prediction models based on player's past performance and social diversity in mentoring network. We use operational data of game players in EverQuest II. Our findings provide a foundation for a customized performance management system and mentor/apprentice recommendation system during game play where its primary objective is to evaluate and suggest mentoring-based social interactions in order to optimize player performance. Furthermore, educational research has found virtual environments to be a sound venue for studying learning, collaboration, social participation, literacy in online space, and learning trajectory at the individual level as well as at the group level. The approach and the results presented in this paper also contribute to a better understanding of learners in virtual environments for educational research (i.e. E-learning, education games) with potential applications in student performance assessment, course and curriculum recommendation, and dynamic monitoring of and instant feedback on student performance.
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Journal Papers by Jaideep Srivastava
Papers by Jaideep Srivastava