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

The rapid development of on-line social networking sites has dramatically changed the way people live and communicate. One particularly interesting phenomena came along with this development is the prominent role of various on-line networking portals played in scheduling and organizing off-line group events and activities. In this paper, we focus on studying the face-to-face(f2f) group formed through, or facilitated by, on-line portals. We first show the distinct characteristics of such f2f groups by analyzing datasets collected from Whrrl and Meetup. Next, we propose a dynamic model for group gathering based on the process of friend invitation to interpret how a f2f group is formed on-line. The results of our model are confirmed by empirical observations. Finally, we demonstrate that using such group information can effectively improve the accuracies of social tie inference and friend recommendation.

From face-to-face gathering to social structure

While online social networks have become a part of many people's daily lives, Internet and social network addictions (ISNAs) have been noted recently. With increased patients in addictive Internet use, clinicians often form support groups to help patients. This has become a trend because groups organized around therapeutic goals can effectively enrich members with insight and guidance while holding everyone accountable along the way. With the emergence of online social network services, there is a trend to form support groups online with the aid of mental health professionals. Nevertheless, it becomes impractical for a psychiatrist to manually select the group members because she faces an enormous number of candidates, while the selection criteria are also complicated since they span both the social and symptom dimensions. To effectively address the need of mental healthcare professionals, this paper makes the first attempt to study a new problem, namely Member Selection for Online Support Group (MSSG). The problem aims to maximize the similarity of the symptoms of all selected members, while ensuring that any two members are unacquainted to each other. We prove that MSSG is NP-Hard and inapproximable within any ratio, and design a 3-approximation algorithm with a guaranteed error bound. We evaluate MSSG via a user study with 11 mental health professionals, and the results manifest that MSSG can effectively find support group members satisfying the member selection criteria. Experimental results on large-scale real datasets also demonstrate that our proposed algorithm outperforms other baselines in terms of solution quality and efficiency.

Forming Online Support Groups for Internet and Behavior Related Addictions

In this paper, we ask whether XML access control can be supported when underlying (XML or relational) storage system does not provide adequate security features and propose three alternative solutions --primitive, pre-processing, and post-processing. Toward that scenario, in particular, we advocate a scalable and effective pre-processing approach, called QFilter. QFilter is based on non-deterministic finite automata (NFA) and rewrites user's queries such that parts violating access control rules are pre-pruned. Through analysis and experimental validation, we show that (1) QFilter guarantees that only permissible portion of data is returned to the authorized users, (2) such access controls can be efficiently enforced without relying on security features of underlying storage system, and (3) such independency makes QFilter capable of many emerging applications, such as in-network access control and access control outsourcing.

/pdf/qfilter-rewriting-insecure-xml-queries-to-secure-ones-using-3qzic9wpxp.pdf

QFilter: rewriting insecure XML queries to secure ones using non-deterministic finite automata

Materialized XPath access-control views are commonly used for enforcing access control. When access control rules defining a materialized XML access-control view change, the view must be adapted to reflect these changes. The process of updating a materialized view after its definition changes is referred to as view adaptation. While XPath security views have been widely reported in literature, the problem of view adaptation for XPath security views has not been addressed. View adaptation results in view downtime during which users are denied access to security views to prevent unauthorized access. Thus, efficient view adaptation is important for making XPath security views pragmatic. In this work, we show how to adapt an XPath access-control view incrementally by re-using the existing view, which reduces computation and communication costs significantly, and results in less downtime for the end-user. Empirical evaluations confirm that the incremental view adaptation algorithms presented in this paper are efficient and scalable.

http://www.cse.psu.edu/~wul2/Publications/wlee%20asiaccs07.pdf

Incremental adaptation of XPath access control views

Online social networking platforms have become a popular channel of communications among people. However, most people can only keep in touch with a limited number of friends. This phenomenon results in a low-connectivity social network in terms of communications, which is inefficient for information propagation and social engagement. In this paper, we introduce a new recommendation service, called link revival, that suggests users to re-connect with their old friends, such that the resulted connection will improve the social network connectivity. To achieve high connectivity improvement under the dynamic social network evolvement, we propose a graph prediction-based recommendation strategy, which selects proper candidates based on the prediction of their future behaviors. We then develop an effective model that exploits non-homogeneous Poisson process and second-order self-similarity in prediction. Through comprehensive experimental studies on two real datasets (Phone Call Network and Facebook Wall-posts), we demonstrate that our proposed approach can significantly increase the social network connectivity, and that the approach outperforms other baseline solutions. The results also show that our solution is more suitable for online social networks like Facebook, partially due to the stronger long range dependency and lower communication costs in the interactions.

Wang-Chien Lee

Papers

From face-to-face gathering to social structure

Forming Online Support Groups for Internet and Behavior Related Addictions

QFilter: rewriting insecure XML queries to secure ones using non-deterministic finite automata

Incremental adaptation of XPath access control views

Boosting social network connectivity with link revival