Detecting highly overlapping community structure by greedy clique expansion

In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE's good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.Comment: 10 pages, 7 Figures. Implementation source and binaries available at http://sites.google.com/site/greedycliqueexpansion

Social Supports in HE: a Social Network Analysis (SNA) Approach to Understanding Learning Experience

In this study, we combined social network analysis with mixed methods approaches to examine social roles and network patterns in social network data gathered from one complete class group of learners in a higher education setting. We investigated the strength of social ties to examine the support activity of this learner sub-group, with a particular comparison between mature learners and other learners, and female and male learners. We also interrogated the meaning of online social relationships, the social strength of online ties and the relation between the existence of a tie to the expectation that the associated individuals have about the implied relationship. We used these analyses to draw inferences about support networks on which these learners rely, considering how learners in an education setting access (or not) the supports which they require. We also considered whether successful access to such supports is influenced by a learner\u27s position in the social structure and whether accessing such supports is considered by the learners themselves to have a significant impact on their experience in HE. A central methodology included the use of a clustering algorithm to carry out a role analysis that categorised the learners into groups, according to the structure of their support networks. Our study considered both age and gender as defining characteristics and discovered social isolation within the network but also social integrators, that is, individuals, or networks of individuals, who are key to functioning support networks. We hope that the findings of this study will help in the understanding of the role of socials supports and provide insights into the learner experience in HE

Preferences over the Fair Division of Goods: Information, Good, and Sample Effects in a Health Context

Greater recognition by economists of the influential role that concern for distributional equity exerts on decision making in a variety of economic contexts has spurred interest in empirical research on the public judgments of fair distribution. Using a stated-preference experimental design, this paper contributes to the growing literature on fair division by investigating the empirical support for each of five distributional principles — equal division among recipients, Rawlsian maximin, total benefit maximization, equal benefit for recipients, and allocation according to relative need among recipients — in the division of a fixed bundle of a good across settings that differ with respect to the good being allocated (a health care good — pills, and non-health care but still health-affecting good — apples) and the way that alternative possible divisions of the good are described (quantitative information only, verbal information only, and both). It also offers new evidence on sample effects (university sample vs. community samples) and how the aggregate ranking of principles is affected by alternative vote-scoring methods. We find important information effects. When presented with quantitative information only, support for the division to equalize benefit across recipients is consistent with that found in previous research; changing to verbal descriptions causes a notable shift in support among principles, especially between equal division of the goods and total benefit maximization. The judgments made when presented with both quantitative and verbal information match more closely those made with quantitative-only descriptions rather than verbal-only descriptions, suggesting that the quantitative information dominates. The information effects we observe are consistent with a lack of understanding among participants as to the relationship between the principles and the associated quantitative allocations. We also find modest good effects in the expected direction: the fair division of pills is tied more closely to benefit-related criterion than is the fair division of apples (even though both produce health benefits). We find evidence of only small differences between the university and community samples and important sex-information interactions.Distributive justice, equity, resource allocation, health care

Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation

The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected to a filtering operation on the graph spectral domain, the theoretical rationale for why this leads to higher performance on the collaborative filtering problem remains unknown. The presented work makes two contributions. First, we investigate the effect of using graph convolution throughout the user and item representation learning processes, demonstrating how the latent features learned are pushed from the filtering operation into the subspace spanned by the eigenvectors associated with the highest eigenvalues of the normalised adjacency matrix, and how vectors lying on this subspace are the optimal solutions for an objective function related to the sum of the prediction function over the training data. Then, we present an approach that directly leverages the eigenvectors to emulate the solution obtained through graph convolution, eliminating the requirement for a time-consuming gradient descent training procedure while also delivering higher performance on three real-world datasets