Community Structure in the United States House of Representatives

We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st--108th Congresses, with the committees connected by ``interlocks'' or common membership. We examine the community structure in these networks using several methods, revealing strong links between certain committees as well as an intrinsic hierarchical structure in the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 election, in which the Republican party earned majority status in the House for the first time in more than forty years. We also combine our network approach with analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.Comment: 44 pages, 13 figures (some with multiple parts and most in color), 9 tables, to appear in Physica A; new figures and revised discussion (including extra introductory material) for this versio

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

Mining and Analyzing the Italian Parliament: Party Structure and Evolution

The roll calls of the Italian Parliament in the XVI legislature are studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the considered time periods. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.Comment: 27 pages, 14 figure

Analysis of a networked social algorithm for collective selection of a committee of representatives

A recent work by Hern\'andez et al. introduced a networked voting rule supported by a trust-based social network, where indications of possible representatives were based on individuals opinions. Individual contributions went beyond a simple vote-counting and were based on proxy voting. These mechanisms generated a high level of representativeness of the selected committee, weakening the possibility of relations of patronage. By incorporating the integrity of individuals and its perception, here we address the question of the trustability of the resulting committee. Our results show that this voting rule provides high representativeness for small committees with a high level of integrity. Furthermore, the voting system displays robustness to a strategic and untruthful application of the voting algorithm.Comment: 7 pages and 8 figures. Submitted for publication. arXiv admin note: text overlap with arXiv:1801.0539

Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges encode the similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using $k$-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions

An overview study of twitter in the UK local government

Copyright @ 2012 Brunel UniversityMicroblogging applications are becoming a momentous element of the public sector social media agenda. The potential of Twitter to update the public with frequent, concise and real-time content has motivated many pubic authorities to create their accounts, thus generating an interesting topic for research. This paper seeks to make an empirical and methodological contribution to this new body of knowledge by presenting an overview study of general Twitter accounts maintained by UK local government authorities. Over 296,000 tweets were collected from the 187officially listed local government accounts. The analysis was conducted in two stages: an examination of the Twitter networks developed by the accounts was followed by a structural analysis of the tweets. The combination of online research and social media analytics techniques enabled us to reach important conclusions about the use of Twitter by those authorities. The findings indicate high level of maturity of Twitter in the UK local government and point to several directions for further increasing the impact and visibility of those accounts within a social media strategy

Persistent homology of time-dependent functional networks constructed from coupled time series

We use topological data analysis to study "functional networks" that we construct from time-series data from both experimental and synthetic sources. We use persistent homology with a weight rank clique filtration to gain insights into these functional networks, and we use persistence landscapes to interpret our results. Our first example uses time-series output from networks of coupled Kuramoto oscillators. Our second example consists of biological data in the form of functional magnetic resonance imaging (fMRI) data that was acquired from human subjects during a simple motor-learning task in which subjects were monitored on three days in a five-day period. With these examples, we demonstrate that (1) using persistent homology to study functional networks provides fascinating insights into their properties and (2) the position of the features in a filtration can sometimes play a more vital role than persistence in the interpretation of topological features, even though conventionally the latter is used to distinguish between signal and noise. We find that persistent homology can detect differences in synchronization patterns in our data sets over time, giving insight both on changes in community structure in the networks and on increased synchronization between brain regions that form loops in a functional network during motor learning. For the motor-learning data, persistence landscapes also reveal that on average the majority of changes in the network loops take place on the second of the three days of the learning process.Comment: 17 pages (+3 pages in Supplementary Information), 11 figures in many text (many with multiple parts) + others in SI, submitte