Data Mining - Concepts and Techniques.

Statistics for Spatial Data.

Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

/pdf/friendship-and-mobility-user-movement-in-location-based-2u0rrxuc9l.pdf

Friendship and mobility: user movement in location-based social networks

The theory of affordances

A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

Temporal Networks

There are millions of accounts in Twitter. In this paper, we categorize twitter accounts into two types, namely Personal Communication Account (PCA) and Public Dissemination Account (PDA). PCAs are accounts operated by individuals and are used to express that individual’s thoughts and feelings. PDAs, on the other hand, refer to accounts owned by non-individuals such as companies, governments, etc. Generally, Tweets in PDA (i) disseminate a specific type of information (e.g., job openings, shopping deals, car accidents) rather than sharing an individual’s personal life; and (ii) may be produced by non-human entities (e.g., bots). We aim to develop techniques for identifying PDAs so as to (i) facilitate social scientists to reduce “noise” in their study of human behaviors, and (ii) to index them for potential recommendation to users looking for specific types of information. Through analysis, we find these two types of accounts follow different temporal, spatial and textual patterns. Accordingly we develop probabilistic models based on these features to identify PDAs. We also conduct a series of experiments to evaluate those algorithms for cleaning the Twitter data stream.

Two Sides of a Coin: Separating Personal Communication and Public Dissemination Accounts in Twitter

In recent years, location-based social networks (LBSNs) have received high attention. While this new breed of social networks is nascent, there is no large-scale analysis conducted to investigate the associations among users in locales of the network. In this paper, we propose four locale based metrics, including Locale Clustering Coefficient, Inward Locale Transitivity, Locale Assortativity Coefficient, and Locale Assortability Coefficient to make association analysis on EveryTrail, a popular LBSN specialized on sharing trips. Based on the analysis result, we observe that people who share more trajectories will get more attention by other users, and people who are popular will connect to the people who are also popular.

/pdf/user-association-analysis-of-locales-on-location-based-2g72qw6ycg.pdf

User association analysis of locales on location based social networks

http://www.cse.psu.edu/~wul2/Publications/wlee%20JSS.pdf

Round-Eye: A system for tracking nearest surrounders in moving object environments

Today, crowdfunding has emerged as a popular means for fundraising. Among various crowdfunding platforms, reward-based ones are the most well received. However, to the best knowledge of the authors, little research has been performed on rewards. In this paper, we analyze a Kickstarter dataset, which consists of approximately 3K projects and 30K rewards. The analysis employs various statistical methods, including Pearson correlation tests, Kolmogorov-Smirnow test and Kaplan-Meier estimation, to study the relationships between various reward characteristics and project success. We find that projects with more rewards, with limited offerings and late-added rewards are more likely to succeed.

Analysis of rewards on reward-based crowdfunding platforms

On a road network, the minimum cost path (or min-cost path for short) from a source location to a destination is a path with the smallest travel cost among all possible paths. Despite that min-cost path queries on static networks have been well studied, the problem of monitoring min-cost paths on a road network in presence of updates is not fully explored. In this paper, we present PathMon, an efficient system for monitoring min-cost paths in dynamic road networks. PathMon addresses two important issues of the min-cost path monitoring problem, namely, (i) path invalidation that identifies min-cost paths returned to path queries affected by network changes, and (ii) path update that replaces invalid paths with new ones for those affected path queries. For (i), we introduce the notion of query scope, based on which a query scope index (QSI) is developed to identify affected path queries. For (ii), we devise a partial path computation algorithm (PPCA) to quickly recompute the updated paths. Through a comprehensive performance evaluation by simulation, QSI and PPCA are demonstrated to be effective on the path invalidation and path update issues.

/pdf/monitoring-minimum-cost-paths-on-road-networks-4gwr3ceil1.pdf

Wang-Chien Lee

Papers

Two Sides of a Coin: Separating Personal Communication and Public Dissemination Accounts in Twitter

User association analysis of locales on location based social networks

Round-Eye: A system for tracking nearest surrounders in moving object environments

Analysis of rewards on reward-based crowdfunding platforms

Monitoring minimum cost paths on road networks