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Author

Myunghwan Kim

Other affiliations: Stanford University
Bio: Myunghwan Kim is an academic researcher from LinkedIn. The author has contributed to research in topics: Dynamic network analysis & Network formation. The author has an hindex of 12, co-authored 19 publications receiving 885 citations. Previous affiliations of Myunghwan Kim include Stanford University.

Papers
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Proceedings Article
14 Nov 2011
TL;DR: Experiments show that the approach can effectively recover the network even when about half of the nodes in the network are missing, and the algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach.
Abstract: Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.

242 citations

Book ChapterDOI
13 Dec 2010
TL;DR: The Multiplicative Attribute Graphs (MAG) model proposed in this article captures the interactions between the network structure and the node attributes, where the probability of an edge between a pair of nodes depends on the product of individual attribute-attribute similarities.
Abstract: Large scale real-world network data such as social and information networks are ubiquitous. The study of such networks seeks to find patterns and explain their emergence through tractable models. In most networks, and especially in social networks, nodes have a rich set of attributes associated with them. We present the Multiplicative Attribute Graphs (MAG) model, which naturally captures the interactions between the network structure and the node attributes. We consider a model where each node has a vector of categorical latent attributes associated with it. The probability of an edge between a pair of nodes depends on the product of individual attribute-attribute similarities. The model yields itself to mathematical analysis. We derive thresholds for the connectivity and the emergence of the giant connected component, and show that the model gives rise to networks with a constant diameter. We also show that MAG model can produce networks with either log-normal or power-law degree distributions.

208 citations

Proceedings ArticleDOI
17 Jun 2013
TL;DR: MonitorRank is introduced, an algorithm that can reduce the time, domain knowledge, and human effort required to find the root causes of anomalies in such service-oriented architectures and provides a ranked order list of possible root causes for monitoring teams to investigate.
Abstract: Large-scale websites are predominantly built as a service-oriented architecture. Here, services are specialized for a certain task, run on multiple machines, and communicate with each other to serve a user's request. An anomalous change in a metric of one service can propagate to other services during this communication, resulting in overall degradation of the request. As any such degradation is revenue impacting, maintaining correct functionality is of paramount concern: it is important to find the root cause of any anomaly as quickly as possible. This is challenging because there are numerous metrics or sensors for a given service, and a modern website is usually composed of hundreds of services running on thousands of machines in multiple data centers.This paper introduces MonitorRank, an algorithm that can reduce the time, domain knowledge, and human effort required to find the root causes of anomalies in such service-oriented architectures. In the event of an anomaly, MonitorRank provides a ranked order list of possible root causes for monitoring teams to investigate. MonitorRank uses the historical and current time-series metrics of each sensor as its input, along with the call graph generated between sensors to build an unsupervised model for ranking. Experiments on real production outage data from LinkedIn, one of the largest online social networks, shows a 26% to 51% improvement in mean average precision in finding root causes compared to baseline and current state-of-the-art methods.

99 citations

Proceedings Article
14 Jul 2011
TL;DR: In this article, 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 is presented.
Abstract: 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 develop a scalable variational expectation 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.

87 citations

Proceedings Article
05 Dec 2013
TL;DR: This work proposes a nonparametric multi-group membership model for dynamic networks that captures the evolution of individual node group memberships via a Factorial Hidden Markov model and explains the dynamics of the network structure by explicitly modeling the connectivity structure of groups.
Abstract: Relational data—like graphs, networks, and matrices—is often dynamic, where the relational structure evolves over time A fundamental problem in the analysis of time-varying 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 dynamics at the level of groups of nodes We propose a nonparametric multi-group 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

54 citations


Cited by
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Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

01 Jan 2013

1,098 citations

Journal ArticleDOI
TL;DR: In this paper, the state-of-the-art algorithms for vital node identification in real networks are reviewed and compared, and extensive empirical analyses are provided to compare well-known methods on disparate real networks.

919 citations

Journal ArticleDOI
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.

806 citations