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Uncover TopicSensitive Information Diffusion Networks
 In AISTATS, 2012b
"... Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the to ..."
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Cited by 11 (6 self)
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Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TOPICCASCADE, for topicsensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and realworld data, we show that our method significantly improves over the previous stateoftheart models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes. 1
Estimating diffusion network structures: Recovery conditions, sample complexity & softthresholding algorithm
 In Proc. of the 31st International Conference on Machine Learning (ICML
, 2014
"... Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and ho ..."
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Cited by 6 (3 self)
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Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascadedata and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuoustime diffusion models using an `1regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe O(d3 logN) cascades, where d is the maximum number of parents of a node and N is the total number of nodes. Moreover, we develop a simple and efficient softthresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.
Parameter Learning for Latent Network Diffusion
"... Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the underlying parameters that govern such a diffusion process by observing the time at which nodes become act ..."
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Cited by 2 (1 self)
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Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the underlying parameters that govern such a diffusion process by observing the time at which nodes become active. A key advantage of our approach is that, unlike previous work, it can tolerate missing observations for some nodes in the diffusion process. Having incomplete observations is characteristic of offline networks used to model the spread of wildlife. We develop an EM algorithm to address parameter learning in such settings. Since both the E and M steps are computationally challenging, we employ a number of optimization methods such as nonlinear and differenceofconvex programming to address these challenges. Evaluation of the approach on the Redcockaded Woodpecker conservation problem shows that it is highly robust and accurately learns parameters in various settings, even with more than 80 % missing data. 1
THE STRUCTURE AND DYNAMICS OF LARGE SOCIAL NETWORKS
, 2013
"... In this thesis, we first explore two different approaches to efficient community detection that address different aspects of community structure. We establish the definition of community fundamentally different from previous literature, where communities were typically assumed to be densely connecte ..."
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In this thesis, we first explore two different approaches to efficient community detection that address different aspects of community structure. We establish the definition of community fundamentally different from previous literature, where communities were typically assumed to be densely connected internally but sparsely connected to the rest of the network. A community should be considered as a densely connected subgraph in which the probability of an edge between any two vertices is higher than average. Further, a community should also be well connected to the remaining network, that is, the number of edges connecting a community to the rest of the graph should be significant. In order to identify a welldefined community, we provide rigorous definitions of two terms: “whiskers ” and the “core”. Whiskers correspond to subsets of vertices that are barely connected to the rest of the network, while the core exclusively contains the type of community we are interested in. We prove that detecting whiskers, or equivalently, extracting the core, is an NPcomplete problem for both weighted and unweighted graphs. Then, three heuristic algorithms
Inferring Diffusion Networks with Sparse Cascades by Structure Transfer
"... Abstract. Inferring diffusion networks from traces of cascades has been intensively studied to gain a better understanding of information diffusion. Traditional methods normally formulate a generative model to find the network that can generate the cascades with the maximum likelihood. The performa ..."
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Abstract. Inferring diffusion networks from traces of cascades has been intensively studied to gain a better understanding of information diffusion. Traditional methods normally formulate a generative model to find the network that can generate the cascades with the maximum likelihood. The performance of such methods largely depends on sufficient cascades spreading in the network. In many realworld scenarios, however, the cascades may be rare. The very sparse data make accurately inferring the diffusion network extremely challenging. To address this issue, in this paper we study the problem of transferring structure knowledge from an external diffusion network with sufficient cascade data to help infer the hidden diffusion network with sparse cascades. To this end, we first consider the network inference problem from a new angle: link prediction. This transformation enables us to apply transfer learning techniques to predict the hidden links with the help of a large volume of cascades and observed links in the external network. Meanwhile, to integrate the structure and cascade knowledge of the two networks, we propose a unified optimization framework TrNetInf. We conduct extensive experiments on two realworld datasets: MemeTracker and Aminer. The results demonstrate the effectiveness of the proposed TrNetInf in addressing the network inference problem with insufficient cascades.
Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence MonteCarlo Expectation Maximization for Decentralized POMDPs
"... We address two significant drawbacks of stateoftheart solvers of decentralized POMDPs (DECPOMDPs): the reliance on complete knowledge of the model and limited scalability as the complexity of the domain grows. We extend a recently proposed approach for solving DECPOMDPs via a reduction to the ma ..."
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We address two significant drawbacks of stateoftheart solvers of decentralized POMDPs (DECPOMDPs): the reliance on complete knowledge of the model and limited scalability as the complexity of the domain grows. We extend a recently proposed approach for solving DECPOMDPs via a reduction to the maximum likelihood problem, which in turn can be solved using EM. We introduce a modelfree version of this approach that employs MonteCarlo EM (MCEM). While a naïve implementation of MCEM is inadequate in multiagent settings, we introduce several improvements in sampling that produce highquality results on a variety of DECPOMDP benchmarks, including large problems with thousands of agents. 1
Learning and Controlling Network Diffusion in Dependent Cascade Models
"... Abstract—Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edg ..."
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Abstract—Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion dynamics parameters and (b) taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved at each network node. As a case study, we address learning visitor mobility pattern at a theme park based on observed historical wait times at individual attractions, and use the learned model to plan management actions that reduce wait time at attractions. We test on realworld data from a theme park in Singapore and show that our learning approach can achieve an accuracy close to 80 % for popular attractions, and the decision support algorithm can provide about 1020 % reduction in wait time. I.