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17
Exploring the design space of social networkbased Sybil defense
 In Proceedings of the 4th International Conference on Communication Systems and Network (COMSNETS’12
, 2012
"... Abstract—Recently, there has been significant research interest in leveraging social networks to defend against Sybil attacks. While much of this work may appear similar at first glance, existing social networkbased Sybil defense schemes can be divided into two categories: Sybil detection and Sybil ..."
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Abstract—Recently, there has been significant research interest in leveraging social networks to defend against Sybil attacks. While much of this work may appear similar at first glance, existing social networkbased Sybil defense schemes can be divided into two categories: Sybil detection and Sybil tolerance. These two categories of systems both leverage global properties of the underlying social graph, but they rely on different assumptions and provide different guarantees: Sybil detection schemes are applicationindependent and rely only on the graph structure to identify Sybil identities, while Sybil tolerance schemes rely on applicationspecific information and leverage the graph structure and transaction history to bound the leverage an attacker can gain from using multiple identities. In this paper, we take a closer look at the design goals, models, assumptions, guarantees, and limitations of both categories of social networkbased Sybil defense systems. I.
Canal: Scaling Social NetworkBased Sybil Tolerance Schemes
"... There has been a flurry of research on leveraging social networks to defend against multiple identity, or Sybil, attacks. A series of recent works does not try to explicitly identify Sybil identities and, instead, bounds the impact that Sybil identities can have. We call these approaches Sybil toler ..."
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There has been a flurry of research on leveraging social networks to defend against multiple identity, or Sybil, attacks. A series of recent works does not try to explicitly identify Sybil identities and, instead, bounds the impact that Sybil identities can have. We call these approaches Sybil tolerance; they have shown to be effective in applications including reputation systems, spam protection, online auctions, and content rating systems. All of these approaches use a social network as a credit network, rendering multiple identities ineffective to an attacker without a commensurate increase in social links to honest users (which are assumed to be hard to obtain). Unfortunately, a hurdle to practical adoption is that Sybil tolerance relies on computationally expensive network analysis, thereby limiting widespread deployment.
Fast fully dynamic landmarkbased estimation of shortest path distances in very large graphs
 In ACM Conference on Information and Knowledge Management (CIKM
, 2011
"... Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of a ..."
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Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of approximate methods have been proposed, including several landmarkbased methods that have been shown to scale up to very large graphs with acceptable accuracy. This paper presents two improvements to existing landmarkbased shortest path estimation methods. The first improvement relates to the use of shortestpath trees (SPTs). Together with appropriate shortcutting heuristics, the use of SPTs allows to achieve higher accuracy with acceptable time and memory overhead. Furthermore, SPTs can be maintained incrementally under edge insertions and deletions, which allows for a fullydynamic algorithm. The second improvement is a new landmark selection strategy that seeks to maximize the coverage of all shortest paths by the selected landmarks. The improved method is evaluated on the DBLP, Orkut, Twitter and Skype social networks.
On kskip Shortest Paths
"... Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertice ..."
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Given two vertices s, t in a graph, let P be the shortest path (SP) from s to t, and P ⋆ a subset of the vertices in P. P ⋆ is a kskip shortest path from s to t, if it includes at least a vertex out of every k consecutive vertices in P. In general, P ⋆ succinctly describes P by sampling the vertices in P with a rate of at least 1/k. This makes P ⋆ a natural substitute in scenarios where reporting every single vertex of P is unnecessary or even undesired. This paper studies kskip SP computation in the context of spatial network databases (SNDB). Our technique has two properties crucial for realtime query processing in SNDB. First, our solution is able to answer kskip queries significantly faster than finding the original SPs in their entirety. Second, the previous objective is achieved with a structure that occupies less space than storing the underlying road network. The proposed algorithms are the outcome of a careful theoretical analysis that reveals valuable insight into the characteristics of the kskip SP problem. Their efficiency has been confirmed by extensive experiments with real data.
Efficient Shortest Paths on Massive Social Graphs
"... Abstract—Analysis of large networks is a critical component of many of today’s application environments, including online social networks, protein interactions in biological networks, and Internet traffic analysis. The arrival of massive network graphs with hundreds of millions of nodes, e.g. social ..."
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Abstract—Analysis of large networks is a critical component of many of today’s application environments, including online social networks, protein interactions in biological networks, and Internet traffic analysis. The arrival of massive network graphs with hundreds of millions of nodes, e.g. social graphs, presents a unique challenge to graph analysis applications. Most of these applications rely on computing distances between node pairs, which for large graphs can take minutes to compute using traditional algorithms such as breadthfirstsearch (BFS). In this paper, we study ways to enable scalable graph processing for today’s massive networks. We explore the design space of graph coordinate systems, a new approach that accurately approximates node distances in constant time by embedding graphs into coordinate spaces. We show that a hyperbolic embedding produces relatively low distortion error, and propose Rigel, a hyperbolic graph coordinate system that lends itself to efficient parallelization across a compute cluster. Rigel produces significantly more accurate results than prior systems, and is naturally parallelizable across compute clusters, allowing it to provide accurate results for graphs up to 43 million nodes. Finally, we show that Rigel’s functionality can be easily extended to locate (near) shortest paths between node pairs. After a onetime preprocessing cost, Rigel answers nodedistance queries in 10’s of microseconds, and also produces shortest path results up to 18 times faster than prior shortestpath systems with similar levels of accuracy. I.
ISLABEL: an IndependentSet based Labeling Scheme for PointtoPoint Distance Querying
"... We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 mil ..."
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We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 million vertices, which is rather small in view of many of the fastgrowing realworld graphs today such as social networks and Web graphs. We propose an efficient index, which is a novel labeling scheme based on the independent set of a graph. We show that our method can handle graphs of size orders of magnitude larger than existing indexes. 1.
Shortest paths in less than a millisecond
 ACM SIGCOMM Workshop on Online Social Networks (WOSN
, 2012
"... We consider the problem of answering pointtopoint shortest path queries on massive social networks. The goal is to answer queries within tens of milliseconds while minimizing the memory requirements. We present a technique that achieves this goal for an extremely large fraction of path queries by ..."
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We consider the problem of answering pointtopoint shortest path queries on massive social networks. The goal is to answer queries within tens of milliseconds while minimizing the memory requirements. We present a technique that achieves this goal for an extremely large fraction of path queries by exploiting the structure of the social networks. Using evaluations on realworld datasets, we argue that our technique offers a unique tradeoff between latency, memory and accuracy. For instance, for the LiveJournal social network (roughly 5 million nodes and 69 million edges), our technique can answer 99.9 % of the queries in less than a millisecond. In comparison to storing all pair shortest paths, our technique requires at least 550 × less memory; the average query time is roughly 365 microseconds — 430 × faster than the stateoftheart shortest path algorithm. Furthermore, the relative performance of our technique improves with the size (and density) of the network. For the Orkut social network (3 million nodes and 220 million edges), for instance, our technique is roughly 2588 × faster than the stateoftheart algorithm for computing shortest paths.
ShortestPath Queries for Complex Networks: Exploiting Low Treewidth Outside the Core
"... We present new and improved methods for efficient shortestpath query processing. Our methods are tailored to work for two specific classes of graphs: graphs with small treewidth and complex networks. Seemingly unrelated at first glance, these two classes of graphs have some commonalities: complex ne ..."
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We present new and improved methods for efficient shortestpath query processing. Our methods are tailored to work for two specific classes of graphs: graphs with small treewidth and complex networks. Seemingly unrelated at first glance, these two classes of graphs have some commonalities: complex networks are known to have a core–fringe structure with a dense core and a treelike fringe. Our main contributions are efficient algorithms and data structures on three different levels. First, we provide two new methods for graphs with small but not necessarily constant treewidth. Our methods achieve new tradeoffs between space and query time. Second, we present an improved treedecompositionbased method for complex networks, utilizing the methods for graphs with small treewidth. Third, we extend our method to handle the highly interconnected core with existing exact and approximate methods. We evaluate our algorithms both analytically and experimentally. We prove that our algorithms for lowtreewidth graphs achieve improved tradeoffs between space and query time. Our experiments on several realworld complex networks further confirm the efficiency of our methods: Both the exact and the hybrid method have faster preprocessing and query times than existing methods. The hybrid method in particular provides an improved tradeoff between space and accuracy.
Mining Differential Hubs in Homogenous Networks
"... Networks have been extensively used to model various complex systems such as online social networks, coauthorship and citation networks and gene networks. Due to different kinds of variations such as temporal, spatial, topic and phenotypic variations, several variants of the same network may exis ..."
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Networks have been extensively used to model various complex systems such as online social networks, coauthorship and citation networks and gene networks. Due to different kinds of variations such as temporal, spatial, topic and phenotypic variations, several variants of the same network may exist. For several practical problems, identifying the nodes that are changing between the networks provide vital information regarding the dynamics of the network states. Given two networks where the nodes are the same in both networks, but the edges are different, we consider the problem of identifying a set of hubs that best explain the differences between the two networks. To the best of our knowledge, this is the first work to address the problem of finding the differential hubs. To address this problem, we propose a novel ranking algorithm, DiffRank, which ranks the nodes of two networks based on their differential behavior between the two networks. We define new measures such as differential connectivity and differential centrality for each node. These measures are propagated through the network and are optimized to capture the local and global structural changes between two networks. We demonstrate the effectiveness of DiffRank on synthetic datasets and realworld applications including collaboration and biological networks. We show that DiffRank identifies meaningful and practically valuable information compared to some of the baseline methods that can be used for such a task.
Shortest Paths in Microseconds
"... Computing shortest paths is a fundamental primitive for several social network applications including sociallysensitive ranking, locationaware search, social auctions and social network privacy. Since these applications compute paths in response to a user query, the goal is to minimize latency whil ..."
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Computing shortest paths is a fundamental primitive for several social network applications including sociallysensitive ranking, locationaware search, social auctions and social network privacy. Since these applications compute paths in response to a user query, the goal is to minimize latency while maintaining feasible memory requirements. We present ASAP, a system that achieves this goal by exploiting the structure of social networks. ASAP preprocesses a given network to compute and store a partial shortest path tree (PSPT) for each node. The PSPTs have the property that for any two nodes, each edge along the shortest path is with high probability contained in the PSPT of at least one of the nodes. We show that the structure of social networks enable the PSPT of each node to be an extremely small fraction of the entire network; hence, PSPTs can be stored efficiently and each shortest path can be computed extremely quickly. For a real network with 5 million nodes and 69 million edges, ASAP computes a shortest path for most node pairs in less than 49 microseconds per pair. ASAP, unlike any previous technique, also computes hundreds of paths (along with corresponding distances) between any node pair in less than 100 microseconds. Finally, ASAP admits efficient implementation on distributed programming frameworks like MapReduce. 1.