Xiaohan Zhao

On the bursty evolution of online social networks

The high level of dynamics in todays online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of a large social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity.

On the validity of geosocial mobility traces

Mobile networking researchers have long searched for large-scale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces.

Phoenix: Towards an Accurate, Practical and Decentralized Network Coordinate System

Network coordinate (NC) system allows efficient Internet distance prediction with scalable measurements. Most of the NC systems are based on embedding hosts into a low dimensional Euclidean space. Unfortunately, the accuracy of predicted distances is largely hurt by the persistent occurrence of Triangle Inequality Violation (TIV) in measured Internet distances. IDES is a dot product based NC system which can tolerate the constraints of TIVs. However, it cannot guarantee the predicted distance non-negative and its prediction accuracy is close to the Euclidean distance based NC systems. In this paper, we propose Phoenix, an accurate, practical and decentralized NC system. It adopts a weighted model adjustment to achieve better prediction accuracy while it ensures the predicted distances to be positive and usable. Our extensive Internet trace based simulation shows that Phoenix can achieve higher prediction accuracy than other representative NC systems. Furthermore, Phoenix has fast convergence and robustness over measurement anomalies.

SLINCS: A Social Link Based Evaluation System for Network Coordinate Systems

In recent research work of securing Network Coordinate (NC) system, they concentrate on the passive security defense mechanisms. In this paper we propose SLINCS, a social link based evaluation security system that utilizes information from existing social relationship networks to implement proactive security mechanisms for NC systems. The key idea is to eliminate suspicious nodes before they launch potential attacks.

Self-Similarity in Social Network Dynamics

Analyzing and modeling social network dynamics are key to accurately predicting resource needs and system behavior in online social networks. The presence of statistical scaling properties, that is, self-similarity, is critical for determining how to model network dynamics. In this work, we study the role that self-similarity scaling plays in a social network edge creation (that is, links created between users) process, through analysis of two detailed, time-stamped traces, a 199 million edge trace over 2 years in the Renren social network, and 876K interactions in a 4-year trace of Facebook. Using wavelet-based analysis, we find that the edge creation process in both networks is consistent with self-similarity scaling, once we account for periodic user activity that makes edge creation process non-stationary. Using these findings, we build a complete model of social network dynamics that combines temporal and spatial components. Specifically, the temporal behavior of our model reflects self-similar scaling properties, and accounts for certain deterministic non-stationary features. The spatial side accounts for observed long-term graph properties, such as graph distance shrinkage and local declustering. We validate our model against network dynamics in Renren and Facebook datasets, and show that it succeeds in producing desired properties in both temporal patterns and graph structural features.

Attacks against Network Coordinate System: Vulnerable PIC

In recent years, network coordinate systems which map nodes into a geometrical space can effectively support overlay applications relying on topology-awareness. However, these systems base on an ideal assumption that the nodes in them are honest to cooperate with each other. Although there have been some studies about attacks on network coordinate systems, the effect of attacks on PIC---one of the representative systems---has not been studied. Moreover, since PIC itself has proposed a security policy, how well it can protect PIC from attacks is another significant problem to be researched. We apply four typical attacks on PIC with security and without security. Our extensive experiments show that PIC is vulnerable by attacks and when the percentage of malicious nodes is more than 40%, PIC with security performs barely better than without security.