Steve Gregory

An Algorithm to Find Overlapping Community Structure in Networks

Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newmans well-known algorithm based on the betweennesscentrality measure. Like the original algorithm, ours performs hierarchical clustering -- partitioning a network into any desired number of clusters -- but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.

A Fast Algorithm to Find Overlapping Communities in Networks

Many networks possess a community structure, such that vertices form densely connected groups which are more sparsely linked to other groups. In some cases these groups overlap, with some vertices shared between two or more communities. Discovering communities in networks is a computationally challenging task, especially if they overlap. In previous work we proposed an algorithm, CONGA, that could detect overlapping communities using the new concept of split betweenness. Here we present an improved algorithm based on a localform of betweenness, which yields good results but is much faster. It is especially effective in discovering small-diameter communities in large networks, and has a time complexity of only O(nlog n) for sparse networks.

Finding Overlapping Communities Using Disjoint Community Detection Algorithms

Many algorithms have been designed to discover community structure in networks. Most of these detect disjoint communities, while a few can find communities that overlap. We propose a new, two-phase, method of detecting overlapping communities. In the first phase, a network is transformed to a new one by splitting vertices, using the idea of split betweenness; in the second phase, the transformed network is processed by a disjoint community detection algorithm. This approach has the potential to convert any disjoint community detection algorithm into an overlapping community detection algorithm. Our experiments, using several “disjoint” algorithms, demonstrate that the method works, producing solutions, and execution times, that are often better than those produced by specialized “overlapping” algorithms.

Balanced multi-label propagation for overlapping community detection in social networks

In this paper, we propose a balanced multi-label propagation algorithm (BMLPA) for overlapping community detection in social networks. As well as its fast speed, another important advantage of our method is good stability, which other multi-label propagation algorithms, such as COPRA, lack. In BMLPA, we propose a new update strategy, which requires that community identifiers of one vertex should have balanced belonging coefficients. The advantage of this strategy is that it allows vertices to belong to any number of communities without a global limit on the largest number of community memberships, which is needed for COPRA. Also, we propose a fast method to generate “rough cores”, which can be used to initialize labels for multi-label propagation algorithms, and are able to improve the quality and stability of results. Experimental results on synthetic and real social networks show that BMLPA is very efficient and effective for uncovering overlapping communities.

The anthropometry of children and adolescents may be influenced by the prenatal smoking habits of their grandmothers: A longitudinal cohort study

Previously, in the Avon Longitudinal Study of Parents and Children (ALSPAC), we have shown different sex‐specific birth anthropometric measurements contingent upon whether or not prenatal smoking was undertaken by paternal grandmother (PGM±), maternal grandmother (MGM±), and the study mother (M±). The findings raised the question as to whether there were long‐term associations on the growth of the study children over time.

Link prediction with node clustering coefficient

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai–Alanis–Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

Associations between prenatal mercury exposure and early child development in the ALSPAC study.

Highlights • Prenatal blood mercury was measured in >2000 women linked their offspring’s development at 4 time points.• Adjusted associations indicated that the higher the level of blood mercury the more advanced the child’s development.• Adjustment of the analyses for blood selenium did not alter the results.• Separate analyses of women who consumed or did not consume fish produced similar results.• There was no indication that child development is harmed by the levels of mercury we studied (up to the EPA’s RFD).

Derivation of Concurrent Algorithms in Tempo

Tempo is a logic programming language that has recently been designed with the aim of allowing concurrent programs to be written in a more declarative manner than existing languages. One of the benefits of declarative programming is the potential for systematic derivation of programs -- the subject of this paper. Here we present a few transformation rules that can be applied to Tempo programs, and then describe in detail the derivation of some concurrent algorithms in Tempo. An outline of the Tempo language is also included in order to make the paper self-contained.

Constructing minimum path configurations for multiprocessor systems

Abstract The performance of a system of distributed memory multiprocessors depends largely on the efficiency of the message transfer system that connects the co-operating processors, in particular, the processor interconnection strategy. For complex science and engineering applications, frequent global communication is required between a processor and every other processor, so it is desirable to minimize the distance between any two processors in the system. Minimum path (AMP) configurations have been proposed as an interconnection strategy to achieve this, and have been shown to provide smaller interprocessor distances than other popular configurations. A disadvantage of AMP configurations is that they are irregular in nature and so their construction is not trivial. In this paper we present an algorithm which uses search techniques to generate AMP configurations for any specified number of processors. It features several optimizations that reduce the search space and direct the search in order that a good solution (i.e. with small interprocessor distances) may be found in a reasonable time. The algorithm has been implemented in Prolog and the program is in regular use to construct AMP configurations which are used to interconnect networks of transputers for the solution of engineering problems.

A prolog-based language for workflow programming

Workflow management systems control activities that are performed in a distributed manner by a number of human or automated participants. There is a wide variety of workflow systems in use, mostly commercial products, and no standard language has been defined in which to express workflow specifications. In this paper we propose Workflow Prolog, a new extension of Prolog. The language allows workflow systems to be implemented in a novel declarative style, while preserving the existing properties of Prolog, such as its familiarity and efficiency. We then demonstrate the expressiveness of the language by showing how it can express each of the workflow patterns that have previously been identified as the requirements of a workflow language.