Inferring the Maximum Likelihood Hierarchy in Social Networks

Maiya, Arun S.; Berger-Wolf, Tanya Y.

doi:10.1109/cse.2009.235

Cited by 36 publications

(31 citation statements)

References 12 publications

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“…The label of X can be one or multiple roles. Representative studies are mining the roles of email communicators (Freeman 1997;Leuski 2004;McCallum et al 2005;Rowe et al 2007), inferring social hierarchy in an organization (Memon et al 2008;Maiya and Berger-Wolf 2009), discovering overlapping roles in synthetic data (Wolfe and Jensen 2004), and mining different types of relationships in online social network (Leskovec et al 2010;Tang et al 2011;Wang et al 2012). …”

Section: Introductionmentioning

confidence: 99%

Relative Validity Criteria for Community Mining Algorithms

Rabbany¹,

Takaffoli²,

Fagnan³

et al. 2018

Encyclopedia of Social Network Analysis and Mining

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Section: Introductionmentioning

confidence: 99%

Relative Validity Criteria for Community Mining Algorithms

Rabbany¹,

Takaffoli²,

Fagnan³

et al. 2018

Encyclopedia of Social Network Analysis and Mining

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“…Typically, this structure or ranking is not known; instead we only have observed outcomes of the interactions, and the goal is to infer the hierarchy from these observations. Discovering hierarchies or ranking has applications in various domains: (i) ranking individual players or teams based on how well they play against each other [4], (ii) discovering dominant animals within a single herd, or ranking species based on who-eats-who networks [9], (iii) inferring hierarchy in work-places, such as, U.S. administration [12], (iv ) summarizing browsing behaviour [11], (v ) discovering hierarchy in social networks [7], for example, if we were to rank twitter users, the top-tier users would be the content-providers, middle-tiers would spread the content, while the bottom-tier are the consumers.…”

Section: Introductionmentioning

confidence: 99%

Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks

Tatti

2016

Data Min Knowl Disc

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Interactions in many real-world phenomena can be explained by a strong hierarchical structure. Typically, this structure or ranking is not known; instead we only have observed outcomes of the interactions, and the goal is to infer the hierarchy from these observations. Discovering a hierarchy in the context of directed networks can be formulated as follows: given a graph, partition vertices into levels such that, ideally, there are only edges from upper levels to lower levels. The ideal case can only happen if the graph is acyclic. Consequently, in practice we have to introduce a penalty function that penalizes edges violating the hierarchy. A practical variant for such penalty is agony, where each violating edge is penalized based on the severity of the violation. Hierarchy minimizing agony can be discovered in O m 2 time, and much faster in practice. In this paper we introduce several extensions to agony. We extend the definition for weighted graphs and allow a cardinality constraint that limits the number of levels. While, these are conceptually trivial extensions, current algorithms cannot handle them, nor they can be easily extended. We solve the problem by showing the connection to the capacitated circulation problem, and we demonstrate that we can compute the exact solution fast in practice for large datasets. We also introduce a provably fast heuristic algorithm that produces rankings with competitive scores. In addition, we show that we can compute agony in polynomial time for any convex penalty, and, to complete the picture, we show that minimizing hierarchy with any concave penalty is an NP-hard problem.

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“…Leskovec et al [19] defines the DAG partitioning problem, which is NP-hard, and proposes a class of heuristic methods of finding a parent for each non-rooted node, which can be regarded as finding hierarchical relationships among the phrase quotations in news articles. Kemp and Tenenbaum [17] propose to use a generative model to find structure in data, and Maiya and Berger-Wolf [21] apply the similar idea in inferring the social network hierarchy with the maximum likelihood of observing the interactions among the people. In the Web search domain, Yin and Shah [33] studies the problem of building taxonomies of search intents for entity queries based on inferring the "belonging" relationships between them with unsupervised approaches.…”

Section: Related Workmentioning

confidence: 99%

Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links

Wang¹,

Han²,

Li³

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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Objects linking with many other objects in an information network may imply various semantic relationships. Uncovering such knowledge is essential for role discovery, data cleaning, and better organization of information networks, especially when the semantically meaningful relationships are hidden or mingled with noisy links and attributes. In this paper we study a generic form of relationship along which objects can form a treelike structure, a pervasive structure in various domains. We formalize the problem of uncovering hierarchical relationships in a supervised setting. In general, local features of object attributes, their interaction patterns, as well as rules and constraints for knowledge propagation can be used to infer such relationships. Existing approaches, designed for specific applications, either cannot handle dependency rules together with local features, or cannot leverage labeled data to differentiate their importance. In this study, we propose a discriminative undirected graphical model. It integrates a wide range of features and rules by defining potential functions with simple forms. These functions are also summarized and categorized. Our experiments on three quite different domains demonstrate how to apply the method to encode domain knowledge. The efficacy is measured with both traditional and our newly designed metrics in the evaluation of discovered tree structures.

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Inferring the Maximum Likelihood Hierarchy in Social Networks

Cited by 36 publications

References 12 publications

Relative Validity Criteria for Community Mining Algorithms

Relative Validity Criteria for Community Mining Algorithms

Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks

Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links

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