Similarity analysis and modeling in mobile societies

Abstract

A new generation of "behavior-aware" delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities.We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models.Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling to incorporate accurate behavioral models in the future.

Analysis of a Hypercube-Based Social Feature Multipath Routing in Delay Tolerant Networks

“…Recently a number of DTN routing protocols have attempted to enhance packet delivery by using social contact information [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [25]. Daly and Haahr analyzed the social network characterizing information for routing in DTNs [12].…”

Hypercube-Based Multipath Social Feature Routing in Human Contact Networks

“…Many social-aware approaches have been proposed [2], [6], [7], [8]. Daly and Haahr [17] analyzed the social network characterizing information for routing and proposed SimBet routing scheme based on a node's ego-centric betweenness centrality and a node's social similarity.…”

“…Several social-aware routing schemes have been proposed recently [2], [6], [7], [8]. Most of these approaches consider the trajectory and/or the contact history of mobile nodes.…”

Inferring user relationship from hidden information in WLANs

“…Relationship inference based on behavior similarity is discussed as a new research area. Relationship discovery based on WLAN users' association logs is discussed in [16], [17]. In [18], a study of mobile phone data proves that similar behavior pattern in cell phone data can provide inference of user relationship.…”