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46
Structure and dynamics of molecular networks: A novel paradigm of drug discovery  A . . .
 PHARMACOLOGY THERAPEUTICS
, 2013
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Evolution of SocialAttribute Networks: Measurements, Modeling, and Implications using Google+
, 2012
"... Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the ..."
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Cited by 23 (6 self)
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Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the social network structure with user attributes (e.g., location, employer, communities of interest) can provide a more finegrained understanding of social networks. However, there have been few studies to provide a systematic understanding of these effects at scale. We bridge this gap using a unique dataset collected as the Google+ social network grew over time since its release in late June 2011. We observe novel phenomena with respect to both standard social network metrics and new attributerelated metrics (that we define). We also observe interesting evolutionary patterns as Google+ went from a bootstrap phase to a steady invitationonly stage before a public release. Based on our empirical observations, we develop a new generative model to jointly reproduce the social structure and the node attributes. Using theoretical analysis and empirical evaluations, we show that our model can accurately reproduce the social and attribute structure of real social networks. We also demonstrate that our model provides more accurate predictions for practical application contexts.
Modeling social networks with node attributes using the multiplicative attribute graph model
 In UAI
, 2011
"... Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicati ..."
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Cited by 18 (3 self)
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Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We developascalablevariationalexpectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure. 1
Latent multigroup membership graph model
"... We develop the Latent Multigroup Membership Graph (LMMG) model, a model of networks with rich node feature structure. In the LMMG model, each node belongs to multiple groups and each latent group models the occurrence of links as well as the node feature structure. The LMMG can be used to summarize ..."
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Cited by 15 (3 self)
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We develop the Latent Multigroup Membership Graph (LMMG) model, a model of networks with rich node feature structure. In the LMMG model, each node belongs to multiple groups and each latent group models the occurrence of links as well as the node feature structure. The LMMG can be used to summarize the network structure, to predict links between the nodes, and to predict missing features of a node. We derive efficient inference and learning algorithms and evaluate the predictive performance of the LMMG on several social and document network datasets. 1.
Graphlet decomposition of a weighted network
, 2012
"... We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable algorithm, which combines EM with BronKerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one n ..."
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Cited by 10 (3 self)
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We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable algorithm, which combines EM with BronKerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one network sample. We explore theoretical properties of graphlets, including computational complexity, redundancy and expected accuracy. We test graphlets on synthetic data, and we analyze messaging on Facebook and crime associations in the 19th century.
An InDepth Study of Stochastic Kronecker Graphs
, 2010
"... Graph analysis is playing an increasingly important ..."
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Cited by 10 (2 self)
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Graph analysis is playing an increasingly important
Testing and Modeling Dependencies Between a Network and Nodal Attributes
, 2013
"... Network analysis is often focused on characterizing the dependencies between network relations and nodelevel attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These met ..."
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Cited by 6 (1 self)
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Network analysis is often focused on characterizing the dependencies between network relations and nodelevel attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are unable provide predictions simultaneously for missing attribute and network information. Existing methods that model the attributes and network jointly also assume the data are fully observed. In this article we introduce a unified approach to analysis that addresses these shortcomings. We use a latent variable model to obtain a low dimensional representation of the network in terms of nodespecific network factors and use a test of dependence between the network factors and attributes as a surrogate for a test of dependence between the network and attributes. We propose a formal testing procedure to determine if dependencies exists between the network factors and attributes. We also introduce a joint model for the network and attributes, for use if the test rejects, that can capture a variety of dependence patterns and be used to make inference and predictions for missing observations.
MODEL SELECTION FOR SOCIAL NETWORKS USING GRAPHLETS
"... Abstract. Several network models have been proposed to explain the link structure observed in online social networks. This paper addresses the problem of choosing the model that best fits a given real world network. We implement a model selection method based on unsupervised learning. An alternatin ..."
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Cited by 5 (0 self)
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Abstract. Several network models have been proposed to explain the link structure observed in online social networks. This paper addresses the problem of choosing the model that best fits a given real world network. We implement a model selection method based on unsupervised learning. An alternating decision tree is trained using synthetic graphs generated according to each of the models under consideration. We use a broad array of features, with the aim of representing different structural aspects of the network. Features include the frequency counts of small subgraphs (graphlets) as well as features capturing the degree distribution and small world property. Our method correctly classifies synthetic graphs, and is robust under perturbations of the graphs. We show that the graphlet counts alone are sufficient in separating the training data, indicating that graphlet counts are a good way of capturing network structure. We tested our approach on four Facebook graphs from various American Universities. The models that best fit this data are those that are based on the principle of preferential attachment. 1.
An InDepth Analysis of Stochastic Kronecker Graphs
, 2013
"... Graph analysis is playing an increasingly important role in science and industry. Due to numerous limitations in sharing realworld graphs, models for generating massive graphs are critical for developing better algorithms. In this paper, we analyze the stochastic Kronecker graph model (SKG), which ..."
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Cited by 4 (1 self)
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Graph analysis is playing an increasingly important role in science and industry. Due to numerous limitations in sharing realworld graphs, models for generating massive graphs are critical for developing better algorithms. In this paper, we analyze the stochastic Kronecker graph model (SKG), which is the foundation of the Graph500 supercomputer benchmark due to its favorable properties and easy parallelization. Our goal is to provide a deeper understanding of the parameters and properties of this model so that its functionality as a benchmark is increased. We develop a rigorous mathematical analysis that shows this model cannot generate a powerlaw distribution or even a lognormal distribution. However, we formalize an enhanced version of the SKG model that uses random noise for smoothing. We prove both in theory and in practice that this enhancement leads to a lognormal distribution. Additionally, we provide a precise analysis of isolated vertices, showing that the graphs that are produced by SKG might be quite different than intended. For example, between 50 % and 75 % of the vertices in the Graph500 benchmarks will be isolated. Finally, we show that this model tends to produce extremely small core numbers (compared to most social networks and other real graphs) for common parameter choices.
Nonparametric Multigroup Membership Model for Dynamic Networks
"... Relational data—like graphs, networks, and matrices—is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of timevarying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entitie ..."
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Cited by 4 (0 self)
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Relational data—like graphs, networks, and matrices—is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of timevarying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multigroup membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure of groups. We demonstrate our model’s capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction. 1