35 research outputs found
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
Clustering and Latent Semantic Indexing Aspects of the Nonnegative Matrix Factorization
This paper provides a theoretical support for clustering aspect of the
nonnegative matrix factorization (NMF). By utilizing the Karush-Kuhn-Tucker
optimality conditions, we show that NMF objective is equivalent to graph
clustering objective, so clustering aspect of the NMF has a solid
justification. Different from previous approaches which usually discard the
nonnegativity constraints, our approach guarantees the stationary point being
used in deriving the equivalence is located on the feasible region in the
nonnegative orthant. Additionally, since clustering capability of a matrix
decomposition technique can sometimes imply its latent semantic indexing (LSI)
aspect, we will also evaluate LSI aspect of the NMF by showing its capability
in solving the synonymy and polysemy problems in synthetic datasets. And more
extensive evaluation will be conducted by comparing LSI performances of the NMF
and the singular value decomposition (SVD), the standard LSI method, using some
standard datasets.Comment: 28 pages, 5 figure
Network-based stratification of tumor mutations.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
Network community detection is a hot research topic in network analysis.
Although many methods have been proposed for community detection, most of them
only take into consideration the lower-order structure of the network at the
level of individual nodes and edges. Thus, they fail to capture the
higher-order characteristics at the level of small dense subgraph patterns,
e.g., motifs. Recently, some higher-order methods have been developed but they
typically focus on the motif-based hypergraph which is assumed to be a
connected graph. However, such assumption cannot be ensured in some real-world
networks. In particular, the hypergraph may become fragmented. That is, it may
consist of a large number of connected components and isolated nodes, despite
the fact that the original network is a connected graph. Therefore, the
existing higher-order methods would suffer seriously from the above
fragmentation issue, since in these approaches, nodes without connection in
hypergraph can't be grouped together even if they belong to the same community.
To address the above fragmentation issue, we propose an Edge enhancement
approach for Motif-aware community detection (EdMot). The main idea is as
follows. Firstly, a motif-based hypergraph is constructed and the top K largest
connected components in the hypergraph are partitioned into modules.
Afterwards, the connectivity structure within each module is strengthened by
constructing an edge set to derive a clique from each module. Based on the new
edge set, the original connectivity structure of the input network is enhanced
to generate a rewired network, whereby the motif-based higher-order structure
is leveraged and the hypergraph fragmentation issue is well addressed. Finally,
the rewired network is partitioned to obtain the higher-order community
structure.Comment: 9 pages, 4 figures, Accepted by KDD 1