56,051 research outputs found
Biological network motif detection and evaluation
Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion: We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structura
Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions
In the age of social computing, finding interesting network patterns or
motifs is significant and critical for various areas such as decision
intelligence, intrusion detection, medical diagnosis, social network analysis,
fake news identification, national security, etc. However, sub-graph matching
remains a computationally challenging problem, let alone identifying special
motifs among them. This is especially the case in large heterogeneous
real-world networks. In this work, we propose an efficient solution for
discovering and ranking human behavior patterns based on network motifs by
exploring a user's query in an intelligent way. Our method takes advantage of
the semantics provided by a user's query, which in turn provides the
mathematical constraint that is crucial for faster detection. We propose an
approach to generate query conditions based on the user's query. In particular,
we use meta paths between nodes to define target patterns as well as their
similarities, leading to efficient motif discovery and ranking at the same
time. The proposed method is examined on a real-world academic network, using
different similarity measures between the nodes. The experiment result
demonstrates that our method can identify interesting motifs, and is robust to
the choice of similarity measures
Detecting outlier patterns with query-based artificially generated searching conditions
In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE
An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels
Network motifs, overrepresented small local connection patterns, are assumed to act
as functional meaningful building blocks of a network and, therefore, received considerable
attention for being useful for understanding design principles and functioning of networks.
We present an extension of the original approach to network motif detection in single,
directed networks without vertex labeling to the case of a sample of directed networks
with pairwise different vertex labels. A characteristic feature of this approach to network
motif detection is that subnetwork counts are derived from the whole sample and the
statistical tests are adjusted accordingly to assign significance to the counts. The associated
computations are efficient since no simulations of random networks are involved. The
motifs obtained by this approach also comprise the vertex labeling and its associated
information and are characteristic of the sample. Finally, we apply this approach to
describe the intricate topology of a sample of vertex-labeled networks which originate from
a previous EEG study, where the processing of painful intracutaneous electrical stimuli
and directed interactions within the neuromatrix of pain in patients with major depression
and healthy controls was investigated. We demonstrate that the presented approach yields
characteristic patterns of directed interactions while preserving their important topological
information and omitting less relevant interactions
QuateXelero : an accelerated exact network motif detection algorithm
Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network
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