101070
Mining coherent dense subgraphs across massive biological networks for functional discovery
MOTIVATION
The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks.


RESULTS
We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Compared with previous methods, our approach is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes.


AVAILABILITY
http://zhoulab.usc.edu/CODENSE/
43402198
Mining Closed Relational Graphs with Connectivity Constraints
Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In a relational graph, each node represents a distinct entity while each edge represents a relationship between entities. Various algorithms were developed to discover interesting patterns from a single relational graph (Z. Wu et al., 1993). However, little attention has been paid to the patterns that are hidden in multiple relational graphs. One interesting pattern in relational graphs is frequent highly connected subgraph which can identify recurrent groups and clusters. In social networks, this kind of pattern corresponds to communities where people are strongly associated. For example, if several researchers co-author some papers, attend the same conferences, and refer their works from each other, it strongly indicates that they are studying the same research theme.
2796750
Pattern-Based Specification and Validation of Web Services Interaction Properties
There have been significant efforts in providing semantic descriptions for Web services, including the approach as exemplified by OWL-S. Part of the semantic description in OWL-S is about the interaction process of the service concerned, and adopts a procedural programming style. We argue that this style of description for service interactions is not natural to publishing service behavior properties from the viewpoint of facilitating third-party service composition and analysis. In this paper, we introduce a declarative approach that better supports the specification and use of service interaction properties in the service description and composition process. This approach uses patterns to describe the interaction behavior of a service as a set of constraints. As such, it supports the incremental description of a service’s interaction behavior from the service developer’s perspective, and the easy understanding and analysis of the interaction properties from the service user’s perspective. We also introduce a framework and tool support for monitoring and checking the conformance of the service’s run-time interactions against its specified interaction properties, to test whether the service is used properly and whether the service fulfils its behavioral obligations.
21192
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.
6059661
Mining frequent patterns without candidate generation
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.
In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.
7180776
A survey of architecture design rationale
Many claims have been made about the consequences of not documenting design rationale. The general perception is that designers and architects usually do not fully understand the critical role of systematic use and capture of design rationale. However, there is to date little empirical evidence available on what design rationale mean to practitioners, how valuable they consider it, and how they use and document it during the design process. This paper reports a survey of practitioners to probe their perception of the value of design rationale and how they use and document the background knowledge related to their design decisions. Based on 81 valid responses, this study has discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated barriers to the use and documentation of design rationale. Based on the findings, we conclude that further research is needed to develop methodology and tool support for design rationale capture and usage. Furthermore, we put forward some specific research questions about design rationale that could be further investigated to benefit industry practice.
40646697
HAND: An Overlay Optimization Algorithm in Peer-to-Peer Systems
Recently, peer-to-peer (P2P) model becomes popular due to its outstanding resource sharing ability in large-scale distributed systems. However, the most popular P2P system – unstructured P2P system, suffers greatly from the mismatching between the logical overlay and physical topology. This mismatching problem causes a large volume of redundant traffic, which makes peer-to-peer system far from scalable and degrades their search efficiency dramatically. In this paper we address the mismatching problem in an unstructured P2P architecture by developing a distributed overlay optimizing algorithm – HAND (Hops Adaptive Neighbor Discovery). Through comprehensive simulations, we show that HAND significantly outperforms previous approaches, especially in large-scale P2P systems.