Community structure of the physical review citation network

Chen, P.; Redner, S.

doi:10.1016/j.joi.2010.01.001

Cited by 159 publications

(107 citation statements)

References 35 publications

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“…We emphasize that our algorithm does not prune the network to make computation manageable (18). Rather, it repeatedly subsamples subgraphs at each iteration.…”

Section: Repeatmentioning

confidence: 99%

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Efficient discovery of overlapping communities in massive networks

Gopalan

Blei

2013

Proc. Natl. Acad. Sci. U.S.A.

220

162

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Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive realworld networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. Community detection is important for both exploring a network and predicting connections that are not yet observed. For example, by finding the communities in a large citation graph of scientific articles, we can make hypotheses about the fields and subfields that they contain. By finding communities in a large social network, we can more easily make predictions to individual members about who they might be friends with but are not yet connected to.In this paper, we develop an algorithm that discovers communities in modern real-world networks. The challenge is that real-world networks are massive-they can contain hundreds of thousands or even millions of nodes. We will examine a network of scientific articles that contains 575,000 articles, a network of connected Web pages that contains 875,000 pages, and a network of US patents that contains 3,700,000 patents. Most approaches to community detection cannot handle data at this scale.There are two fundamental difficulties to detecting communities in such networks. The first is that many existing community detection algorithms assume that each node belongs to a single community (1,(3)(4)(5)(6)(7)(14)(15)(16). In real-world networks, each node will likely belong to multiple communities and its connections will reflect these multiple memberships (2,(8)(9)(10)(11)(12)(13)17). For example, in a large social network, a member may be connected to coworkers, friends from school, and neighbors. We need algorithms that discover overlapping communities to capture the heterogeneity of each node's connections.The second difficulty is that existing algorithms are too slow. Many community detection algorithms iteratively analyze each pair of nodes, regardless of whether the nodes in the pair are connected in the network (5, 6, 10). Consequently, these algorithms run in time squared in the number of nodes, which makes analyzing massive networks computationally intractable. Other a...

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“…We emphasize that our algorithm does not prune the network to make computation manageable (18). Rather, it repeatedly subsamples subgraphs at each iteration.…”

Section: Repeatmentioning

confidence: 99%

“…These algorithms have become vital to data-driven methods for understanding and exploring network data such as social networks (4), citation networks (18), communication networks (19), and networks induced by scientific observation [e.g., gene regulation networks (20)]. …”

mentioning

confidence: 99%

Efficient discovery of overlapping communities in massive networks

Gopalan

Blei

2013

Proc. Natl. Acad. Sci. U.S.A.

220

162

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show abstract

“…It mostly apply to academic research, but have found its way in complex network research. Numerous works actually focus on communities [8], and the characterization of the dynamics of the citation graphs [15]. The closest to the spirit of our research would be the work by Hummon and Dereian [21] who studied the main paths in the citation network in order to extract backbones and areas of interest.…”

Section: Related Workmentioning

confidence: 99%

Flows of Knowledge in Citation Networks

Renoust

Claver

Baffier

2016

Complex Networks &Amp; Their Applications V

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Knowledge is created and transmitted through generation. Innovation is often seen as a generative process from collective intelligence, but how does innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherit? A citation network can be seen as a perfect example of a generative process leading to innovation. Inspired by the notion of "stream of knowledge", we propose to look at the question of production of knowledge under the lens of DAGs. Although many works look for the evaluation of publications, we propose to look for production of knowledge within a framework for analyzing DAGs. In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. We propose an efficient dynamic algorithm for integration with modern graph databases, conducting our experiment with the Arxiv HEP-TH dataset. Our results validate the use of DAG flows for citation flows and show evidence of the relevance of the h-index.

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“…We considered a citation to a more recent paper as an error and removed 284 from APS and 20,418 from DBLP, respectively. In addition, we excluded 2,555 review articles from the APS dataset that were published in Reviews of Modern Physics, since their citation patterns are different from regular research papers [23]. Review papers never start a new paradigm or become a challenger by definition and thus are not in the scope of our search.…”

Section: A Datamentioning

confidence: 99%

Identifying Transformative Scientific Research

Huang

Hsu²,

Lerman³

2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a datadriven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger's introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.

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Community structure of the physical review citation network

Cited by 159 publications

References 35 publications

Efficient discovery of overlapping communities in massive networks

Efficient discovery of overlapping communities in massive networks

Flows of Knowledge in Citation Networks

Identifying Transformative Scientific Research

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