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NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks
"... How to spot and summarize anomalies in dynamic networks such as road networks, communication networks and social networks? An anomalous event, such as a traffic accident, a denial of service attack or a chemical spill, can affect several nearby edges and make them behave abnormally, over several co ..."
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How to spot and summarize anomalies in dynamic networks such as road networks, communication networks and social networks? An anomalous event, such as a traffic accident, a denial of service attack or a chemical spill, can affect several nearby edges and make them behave abnormally, over several consecutive timeticks. We focus on spotting and summarizing such significant anomalous regions, spanning space (i.e. nearby edges), as well as time. Our first contribution is the problem formulation, namely finding all such Significant Anomalous Regions (SAR). The next contribution is the design of novel algorithms: an expensive, exhaustive algorithm, as well as an efficient approximation, called NetSpot. Compared to the exhaustive algorithm, NetSpot is up to one order of magnitude faster in real data, while achieving less than 4 % average relative error rate. In synthetic datasets, it is more than 30 times faster and solves large problem instances that are otherwise infeasible. The final contribution is the validation on real data: we demonstrate the utility of NetSpot for inferring accidents on road networks and detecting patterns of anomalous access to subnetworks of Wikipedia. We also study NetSpot’s scalability in large social, transportation and synthetic evolving networks, spanning in total up to 50 million edges. 1
Evolutionary Network Analysis: A Survey
"... Evolutionary network analysis has found an increasing interest in the literature because of the importance of different kinds of dynamic social networks, email networks, biological networks, and social streams. When a network evolves, the results of data mining algorithms such as community detection ..."
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Evolutionary network analysis has found an increasing interest in the literature because of the importance of different kinds of dynamic social networks, email networks, biological networks, and social streams. When a network evolves, the results of data mining algorithms such as community detection need to be correspondingly updated. Furthermore, the specific kinds of changes to the structure of the network, such as the impact on community structure or the impact on network structural parameters, such as node degrees, also needs to be analyzed. Some dynamic networks have a much faster rate of edge arrival and are referred to as network streams or graph streams. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the onepass constraint of data streams. The incorporation of content can add further complexity to the evolution analysis process. This survey provides an overview of the vast literature on graph evolution analysis and the numerous applications that arise in different contexts.
Mining evolving network processes
"... Abstract—Processes within real world networks evolve according to the underlying graph structure. A bounty of examples exists in diverse network genres: botnet communication growth, moving traffic jams [25], information foraging [37] in document networks (WWW and Wikipedia), and spread of viral meme ..."
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Abstract—Processes within real world networks evolve according to the underlying graph structure. A bounty of examples exists in diverse network genres: botnet communication growth, moving traffic jams [25], information foraging [37] in document networks (WWW and Wikipedia), and spread of viral memes or opinions in social networks. The network structure in all the above examples remains relatively fixed, while the shape, size and position of the affected network regions change gradually with time. Traffic jams grow, move, shrink and eventually disappear. Public attention shifts among current hot topics inducing a similar shift of highly accessed Wikipedia articles. Discovery of such smoothly evolving network processes has the potential to expose the intrinsic mechanisms of complex network dynamics, enable new datadriven models and improve network design. We introduce the novel problem of Mining smoothly evolving processes (MINESMOOTH) in networks with dynamic realvalued node/edge weights. We show that ensuring smooth transitions in the solution is NPhard even on restricted network structures such as trees. We propose an efficient filteringbased framework, called LEGATO. It achieves 3−7 times improvement in the obtained process scores (i.e. larger and strongerimpact processes) compared to alternatives on real networks, and above 80 % accuracy in discovering realistic “embedded ” processes in synthetic networks. In transportation networks, LEGATO discovers processes that conform to existing theoretical models for traffic jams, while its obtained processes in Wikipedia reveal the temporal evolution of information seeking of Internet users. I.
gIceberg: Towards Iceberg Analysis in Large Graphs
, 2013
"... Traditional multidimensional data analysis techniques such as iceberg cube cannot be directly applied to graphs for finding interesting or anomalous vertices due to the lack of dimensionality in graphs. In this paper, we introduce the concept of graph icebergs that refer to vertices for which the ..."
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Traditional multidimensional data analysis techniques such as iceberg cube cannot be directly applied to graphs for finding interesting or anomalous vertices due to the lack of dimensionality in graphs. In this paper, we introduce the concept of graph icebergs that refer to vertices for which the concentration (aggregation) of an attribute in their vicinities is abnormally high. Intuitively, these vertices shall be “close ” to the attribute of interest in the graph space. Based on this intuition, we propose a novel framework, called gIceberg, which performs aggregation using random walks, rather than traditional SUM and AVG aggregate functions. This proposed framework scores vertices by their different levels of interestingness and finds important vertices that meet a userspecified threshold. To improve scalability, two aggregation strategies, forward and backward aggregation, are proposed with corresponding optimization techniques and bounds. Experiments on both realworld and synthetic large graphs demonstrate that gIceberg is effective and scalable.
Clustering evolving networks
 CoRR
"... Abstract. Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these addition ..."
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Abstract. Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online scenarios, i.e., approaches that incrementally use structural information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are often used for evaluation. 1
Anomaly detection in dynamic networks: a survey
 Wiley Interdisciplinary Reviews: Computational Statistics
, 2015
"... Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressivene ..."
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Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As realworld networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time. In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the stateoftheart methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general twostage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a twotiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categoriescommunity, compression, decomposition, distance, and probabilistic model basedwe highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches. © 2015 The Authors. financial systems connecting banks across the world, electric power grids connecting geographically distributed areas, and social networks that connect users, businesses, or customers using relationships such as friendship, collaboration, or transactional interactions. These are examples of dynamic networks, which, unlike static networks, are constantly undergoing changes to their structure or attributes. Possible changes include insertion and deletion of vertices (objects), insertion and deletion of edges (relationships), and modification of attributes (e.g., vertex or edge labels). WIREs Computational Statistics An important problem over dynamic networks is anomaly detectionfinding objects, relationships, or
Paths MSS O(E) O(E)
"... 1.1 Example of dynamic network Fig. 1 shows an example of dynamic network and some typical regions. The network is a simple path. The horizontal axis represents the time. Each region spans a connected subnetwork and a time interval. A region may contain negative edges (see region 5), provided that ..."
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1.1 Example of dynamic network Fig. 1 shows an example of dynamic network and some typical regions. The network is a simple path. The horizontal axis represents the time. Each region spans a connected subnetwork and a time interval. A region may contain negative edges (see region 5), provided that its total score is high.
OUTLIER DETECTION FOR INFORMATION NETWORKS
, 2013
"... The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. There has been a significant amount of work in network science which studies properties of networks, querying over networks, link analysis, influence propagation, network optimization, and ma ..."
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The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. There has been a significant amount of work in network science which studies properties of networks, querying over networks, link analysis, influence propagation, network optimization, and many other forms of network analysis. Only recently has there been some work in the area of outlier detection for information network data. Outlier (or anomaly) detection is a very broad field and has been studied in the context of a large number of application domains. Many algorithms have been proposed for outlier detection in highdimensional data, uncertain data, stream data and time series data. By its inherent nature, network data provides very different challenges that need to be addressed in a special way. Network data is gigantic, contains nodes of different types, rich nodes with associated attribute data, noisy attribute data, noisy link data, and is dynamically evolving in multiple ways. This thesis focuses on outlier detection for such networks with respect to two interesting perspectives: (1) community based outliers and (2) query based outliers. For community based outliers, we discuss the problem in both static as well as dynamic settings.
Efficient Nonparametric Subgraph Detection using Tree Shaped Priors
"... Nonparametric graph scan (NPGS) statistics are used to detect anomalous connected subgraphs on graphs, and have a wide variety of applications, such as disease outbreak detection, road traffic congestion detection, and event detection in social media. In contrast to traditional parametric scan st ..."
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Nonparametric graph scan (NPGS) statistics are used to detect anomalous connected subgraphs on graphs, and have a wide variety of applications, such as disease outbreak detection, road traffic congestion detection, and event detection in social media. In contrast to traditional parametric scan statistics (e.g., the Kulldorff statistic), NPGS statistics are free of distributional assumptions and can be applied to heterogeneous graph data. In this paper, we make a number of contributions to the computational study of NPGS statistics. First, we present a novel reformulation of the problem as a sequence of Budget PriceCollecting Steiner Tree (BPCST) subproblems. Second, we show that this reformulated problem is NPhard for a large class of nonparametric statistic functions. Third, we further develop efficient exact and approximate algorithms for a special category of graphs in which the anomalous subgraphs can be reformulated in a fixed tree topology. Finally, using extensive experiments we demonstrate the performance of our proposed algorithms in two realworld application domains (water pollution detection in water sensor networks and spatial event detection in social media networks) and contrast against stateoftheart connected subgraph detection methods. 1
AciForager: Incrementally Discovering Regions of Correlated Change in Evolving Graphs
"... components, fault detection ..."