Community structure in Congressional cosponsorship networks

Zhang, Yan; Friend, A. J.; Traud, Amanda L.; Porter, Mason A.; Fowler, James H.; Mucha, Peter J.

doi:10.1016/j.physa.2007.11.004

Cited by 128 publications

(52 citation statements)

References 25 publications

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“…Additionally, prior empirical studies have observed some correspondence between communities and "ground truth" groups in social and biological networks [33]. For example, communities in social networks might correspond to circles of friends or business associates, communities in the World Wide Web might encompass pages on closely-related topics, communities in metabolic networks have been used to find functional modules [15], and communities have been used to identify and measure political polarization in legislative processes in the U.S. Congress [37,38].…”

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confidence: 99%

“…In addition to remarkable successes on benchmark examples, investigations of community structure have led to success stories in diverse application areasincluding the reconstruction of college football conferences [11] and the investigation of such structures in algorithmic rankings [6]; the analysis of committee assignments [32], legislation cosponsorship [38], and voting blocs [37] in the U.S. Congress; the examination of functional groups in metabolic networks [15]; the study of ethnic preferences in school friendship networks [13]; and the study of social structures in mobile-phone conversation networks [31].…”

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confidence: 99%

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Comparing Community Structure to Characteristics in Online Collegiate Social Networks

Traud¹,

Kelsic

Mucha³

et al. 2011

SIAM Rev.

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391

336

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Abstract. We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the pair-counting indices employed and recall, in simplified notation, a useful analytical formula for the z-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form "communities," and leads to comparative observations about online social lives that can be used to infer comparisons about offline social structures. In our illustration of this methodology, we calculate the relative contributions of different characteristics to the community structure of individual universities and subsequently compare these relative contributions at different universities, measuring for example the importance of common high school affiliation to large state universities and the varying degrees of influence common major can have on the social structure at different universities. The heterogeneity of communities that we observe indicates that these networks typically have multiple organizing factors rather than a single dominant one.

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confidence: 99%

mentioning

confidence: 99%

Comparing Community Structure to Characteristics in Online Collegiate Social Networks

Traud¹,

Kelsic

Mucha³

et al. 2011

SIAM Rev.

Self Cite

391

336

View full text Add to dashboard Cite

show abstract

“…In panel (c), we computed aggregate core scores (11) using the core quality (10) and the transition function (9). measures of political partisanship studied using roll-call voting networks [59]. As one can see from Fig.…”

Section: Voting-similarity Network Of the United States Senatementioning

confidence: 97%

Core-Periphery Structure in Networks

et al. 2012

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Intermediate-scale (or 'meso-scale') structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this paper, we develop a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely-connected core nodes and sparsely-connected periphery nodes. In contrast to communities, the nodes in a core are also reasonably well-connected to those in the periphery. Our new method of computing core-periphery structure can identify multiple cores in a network and takes different possible cores into account. We illustrate the differences between our method and several existing methods for identifying which nodes belong to a core, and we use our technique to examine core-periphery structure in examples of friendship, collaboration, transportation, and voting networks.

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“…Therefore, efficient solutions to the graph cut problem provide a means of doing MAP estimations for these types of MRFs, and is computationally more efficient compared to techniques for generic MRFs such as belief propagation [32,35]. Graph partitioning is also tightly related to the study of networks in statistical physics [21,24,36]. In [18], Hu et al applied methods for solving graph cut problems to perform modularity optimization [16,25,36], a technique widely applied for community detection in networks.…”

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confidence: 99%

“…Graph partitioning is also tightly related to the study of networks in statistical physics [21,24,36]. In [18], Hu et al applied methods for solving graph cut problems to perform modularity optimization [16,25,36], a technique widely applied for community detection in networks.…”

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confidence: 99%

Convergence of the Graph Allen–Cahn Scheme

Luo

Bertozzi

2017

J Stat Phys

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The graph Laplacian and the graph cut problem are closely related to Markov random fields, and have many applications in clustering and image segmentation. The diffuse interface model is widely used for modeling in material science, and can also be used as a proxy to total variation minimization. In Bertozzi and Flenner (Multiscale Model Simul 10(3):1090-1118, 2012), an algorithm was developed to generalize the diffuse interface model to graphs to solve the graph cut problem. This work analyzes the conditions for the graph diffuse interface algorithm to converge. Using techniques from numerical PDE and convex optimization, monotonicity in function value and convergence under an a posteriori condition are shown for a class of schemes under a graph-independent stepsize condition. We also generalize our results to incorporate spectral truncation, a common technique used to save computation cost, and also to the case of multiclass classification. Various numerical experiments are done to compare theoretical results with practical performance.

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Community structure in Congressional cosponsorship networks

Cited by 128 publications

References 25 publications

Comparing Community Structure to Characteristics in Online Collegiate Social Networks

Comparing Community Structure to Characteristics in Online Collegiate Social Networks

Core-Periphery Structure in Networks

Convergence of the Graph Allen–Cahn Scheme

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