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MultiStep Community Detection and Hierarchical Time Segmentation in Evolving Networks
 Proc. of the 5th SNAKDD Workshop Social Network Mining and Analysis, August 21
, 2011
"... Many complex systems composed of interacting objects like social networks or the web can be modeled as graphs. They can usually be divided in dense subgraphs with few links between them, called communities and detecting this underlying community structure may have a major impact in the understandi ..."
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Many complex systems composed of interacting objects like social networks or the web can be modeled as graphs. They can usually be divided in dense subgraphs with few links between them, called communities and detecting this underlying community structure may have a major impact in the understanding of these systems. We focus here on evolving graphs, for which the usual approach is to represent the state of the system at different time steps and to compute communities independently on the graph obtained at each time step. We propose in this paper to use a different framework: instead of detecting communities on each time step, we detect a unique decomposition in communities that is relevant for (almost) every time step during a given period called the time window. We propose a definition of this new decomposition and two algorithms to detect it quickly. We validate both the approach and the algorithms on three evolving networks of different kinds showing that the quality loss at each time step is very low despite the constraint of maximization on several time steps. Since the time window length is a crucial parameter of our technique, we also propose an unsupervised hierarchical clustering algorithm to build automatically a hierarchical time segmentation into time windows. This clustering relies on a new similarity measure based on community structure. We show that it is very efficient in detecting meaningful windows.
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.
Using graph partitioning to discover regions of correlated spatiotemporal change in evolving graphs
 Intell. Data Anal
"... There is growing interest in studying dynamic graphs, or graphs that evolve with time. In this work, we investigate a new type of dynamic graph analysis finding regions of a graph that are evolving in a similar manner and are topologically similar over a period of time. For example, these regions c ..."
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There is growing interest in studying dynamic graphs, or graphs that evolve with time. In this work, we investigate a new type of dynamic graph analysis finding regions of a graph that are evolving in a similar manner and are topologically similar over a period of time. For example, these regions can be used to group a set of changes having a common cause in event detection and fault diagnosis. Prior work [6] has proposed a greedy framework called cSTAG to find these regions. It was accurate in datasets where the regions are temporally and spatially well separated. However, in cases where the regions are not well separated, cSTAG produces incorrect groupings.
Knowledge and Information Systems. vol.26, no.2, pp.309336, 2011. 1 Statistical Outlier Detection Using Direct Density Ratio Estimation
"... We propose a new statistical approach to the problem of inlierbased outlier detection, i.e., finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score. This approach is expected to ..."
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We propose a new statistical approach to the problem of inlierbased outlier detection, i.e., finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score. This approach is expected to have better performance even in highdimensional problems since methods for directly estimating the density ratio without going through density estimation are available. Among various density ratio estimation methods, we employ the method called unconstrained leastsquares importance fitting (uLSIF) since it is equipped with natural crossvalidation procedures, allowing us to objectively optimize the value of tuning parameters such as the regularization parameter and the kernel width. Furthermore, uLSIF offers a closedform solution as well as a closedform formula for the leaveoneout error, so
DEFINING DYNAMIC SPATIOTEMPORAL NEIGHBOURHOOD OF NETWORK DATA
"... To improve the accuracy and efficiency of spacetime analysis, spatiotemporal neighbourhoods (STNs) should be investigated and analysed in the classification, prediction and outlier detection of spacetime data. So far most researches in spacetime analysis use either spatial or temporal neighbourh ..."
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To improve the accuracy and efficiency of spacetime analysis, spatiotemporal neighbourhoods (STNs) should be investigated and analysed in the classification, prediction and outlier detection of spacetime data. So far most researches in spacetime analysis use either spatial or temporal neighbourhoods, without considering both time and space at the same time. Moreover, the neighbourhoods are mostly defined intuitively without quantitative measurement. Furthermore, STNs of network data are less investigated compared with other types of data due to the complexity of network structure. This paper investigates the existing approaches of defining STNs and proposes a quantitative method to define STNs of network data in which the topology of the network does not change but the characteristics of the edges (i.e. thematic attribute values) change with time which requires dynamic STNs adapted to the properties of the network. The proposed method is tested by using London traffic network data. 1.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL., NO., 1 Cascading
, 2011
"... This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. ..."
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
A Query Based Approach for Mining Evolving Graphs
"... An evolving graph is a graph that can change over time. Such graphs can be applied in modelling a wide range of realworld phenomena, like computer networks, social networks and protein interaction networks. This paper addresses the novel problem of querying evolving graphs using spatiotemporal pat ..."
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An evolving graph is a graph that can change over time. Such graphs can be applied in modelling a wide range of realworld phenomena, like computer networks, social networks and protein interaction networks. This paper addresses the novel problem of querying evolving graphs using spatiotemporal patterns. In particular, we focus on answering selection queries, which can discover evolving subgraphs that satisfy both a temporal and a spatial predicate. We investigate the efficient implementation of such queries and experimentally evaluate our techniques using realworld evolving graph datasets Internet connectivity logs and the Enron email corpus. We show that is possible to use queries to discover meaningful events hidden in this data and demonstrate that our implementation is scalable for very large evolving graphs.
AciForager: Incrementally Discovering Regions of Correlated Change in Evolving Graphs
"... components, fault detection ..."