12,617 research outputs found
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.Comment: 6 pages, under submission to HPEC Graph Challeng
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks
The unsupervised detection of anomalies in time series data has important
applications in user behavioral modeling, fraud detection, and cybersecurity.
Anomaly detection has, in fact, been extensively studied in categorical
sequences. However, we often have access to time series data that represent
paths through networks. Examples include transaction sequences in financial
networks, click streams of users in networks of cross-referenced documents, or
travel itineraries in transportation networks. To reliably detect anomalies, we
must account for the fact that such data contain a large number of independent
observations of paths constrained by a graph topology. Moreover, the
heterogeneity of real systems rules out frequency-based anomaly detection
techniques, which do not account for highly skewed edge and degree statistics.
To address this problem, we introduce HYPA, a novel framework for the
unsupervised detection of anomalies in large corpora of variable-length
temporal paths in a graph. HYPA provides an efficient analytical method to
detect paths with anomalous frequencies that result from nodes being traversed
in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM
Data Mining (SDM 2020
Dynamic load balancing for the distributed mining of molecular structures
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of
methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the
past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially
render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to
discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no
reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic
partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated
load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer
Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed
approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable
for large-scale, multi-domain, heterogeneous environments, such as computational grids
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