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28
Graph embedding for speaker recognition
 in Proc. Interspeech, 2010
"... This chapter presents applications of graph embedding to the problem of textindependent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison—given two speech recordings (utterances), produce a score which me ..."
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This chapter presents applications of graph embedding to the problem of textindependent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison—given two speech recordings (utterances), produce a score which measures speaker simi
Deepwalk: Online learning of social representations. arXiv preprint arXiv:1403.6652
, 2014
"... We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and ..."
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Cited by 14 (1 self)
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We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk’s latent representations on several multilabel network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can provide F1 scores up to 10 % higher than competing methods when labeled data is sparse. In some experiments, DeepWalk’s representations are able to outperform all baseline methods while using 60 % less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Asynchronous LargeScale Graph Processing Made Easy
"... Scaling large iterative graph processing applications through parallel computing is a very important problem. Several graph processing frameworks have been proposed that insulate developers from lowlevel details of parallel programming. Most of these frameworks are based on the bulk synchronous par ..."
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Cited by 11 (1 self)
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Scaling large iterative graph processing applications through parallel computing is a very important problem. Several graph processing frameworks have been proposed that insulate developers from lowlevel details of parallel programming. Most of these frameworks are based on the bulk synchronous parallel (BSP) model in order to simplify application development. However, in the BSP model, vertices are processed in fixed rounds, which often leads to slow convergence. Asynchronous executions can significantly accelerate convergence by intelligently ordering vertex updates and incorporating the most recent updates. Unfortunately, asynchronous models do not provide the programming simplicity and scalability advantages of the BSP model. In this paper, we combine the easy programmability of the BSP model with the high performance of asynchronous execution. We have designed GRACE, a new graph programming platform that separates application logic from execution policies. GRACE provides a synchronous iterative graph programming model for users to easily implement, test, and debug their applications. It also contains a carefully designed and implemented parallel execution engine for both synchronous and userspecified builtin asynchronous execution policies. Our experiments show that asynchronous execution in GRACE can yield convergence rates comparable to fully asynchronous executions, while still achieving the nearlinear scalability of a synchronous BSP system. 1.
Transforming Graph Data for Statistical Relational Learning
, 2012
"... Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In th ..."
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Cited by 9 (4 self)
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Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graphbased relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
Investigating markov logic networks for collective classification
 In ICAART
, 2012
"... Collective Classification (CC) is the process of simultaneously inferring the class labels of a set of interlinked nodes, such as the topic of publications in a citation graph. Recently, Markov Logic Networks (MLNs) have attracted significant attention because of their ability to combine first orde ..."
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Cited by 4 (1 self)
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Collective Classification (CC) is the process of simultaneously inferring the class labels of a set of interlinked nodes, such as the topic of publications in a citation graph. Recently, Markov Logic Networks (MLNs) have attracted significant attention because of their ability to combine first order logic with probabilistic reasoning. A few authors have used this ability of MLNs in order to perform CC over linked data, but the relative advantages of MLNs vs. other CC techniques remains unknown. In response, this paper compares a wide range of MLN learning and inference algorithms to the best previously studied CC algorithms. We find that MLN accuracy is highly dependent on the type of learning and the input rules that are used, which is not unusual given MLNs ’ flexibility. More surprisingly, we find that even the best MLN performance generally lags that of the best previously studied CC algorithms. However, MLNs do excel on the one dataset that exhibited the most complex linking patterns. Ultimately, we find that MLNs may be worthwhile for CC tasks involving data with complex relationships, but that MLN learning for such data remains a challenge. 1
Node Clustering in Graphs: An Empirical Study
"... Modeling networks is an active area of research and is used for many applications ranging from bioinformatics to social network analysis. An important operation that is often performed in the course of graph analysis is node clustering. Popular methods for node clustering such as the normalized cut ..."
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Cited by 4 (4 self)
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Modeling networks is an active area of research and is used for many applications ranging from bioinformatics to social network analysis. An important operation that is often performed in the course of graph analysis is node clustering. Popular methods for node clustering such as the normalized cut method have their roots in graph partition optimization and spectral graph theory. Recently, there has been increasing interest in modeling graphs probabilistically using stochastic block models and other approaches that extend it. In this paper, we present an empirical study that compares the node clustering performances of stateoftheart algorithms from both the probabilistic and spectral families on undirected graphs. Our experiments show that no family dominates over the other and that network characteristics play a significant role in determining the best model to use. 1
Evaluating Markov Logic Networks for Collective Classification
"... Collective Classification (CC) is the process of simultaneously inferring the class labels of a set of interlinked nodes, such as the topic of publications in a citation graph. Recently, Markov Logic Networks (MLNs) have attracted significant attention because of their ability to combine first orde ..."
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Cited by 4 (3 self)
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Collective Classification (CC) is the process of simultaneously inferring the class labels of a set of interlinked nodes, such as the topic of publications in a citation graph. Recently, Markov Logic Networks (MLNs) have attracted significant attention because of their ability to combine first order logic with probabilistic reasoning. A few authors have used this ability of MLNs in order to perform CC over linked data, but the relative advantages of MLNs vs. other CC techniques remains unknown. In response, this paper compares a wide range of MLN learning and inference algorithms to the best previously studied CC algorithms. We find that MLN accuracy is highly dependent on the type of learning and the input rules that are used, which is not unusual given MLNs ’ flexibility. More surprisingly, we find that even the best MLN performance generally lags that of the best previously studied CC algorithms. However, MLNs do excel on the one dataset that exhibited the most complex linking patterns. Ultimately, we find that MLNs may be worthwhile for CC tasks involving data with complex relationships, but that MLN learning for such data remains a challenge. 1.
Querybyexample using speaker content graphs
 in Proc. Interspeech
, 2012
"... We describe methods for constructing and using content graphs for querybyexample speaker recognition tasks within a large speech corpus. This goal is achieved as follows: First, we describe an algorithm for constructing speaker content graphs, where nodes represent speech signals and edges repres ..."
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Cited by 4 (3 self)
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We describe methods for constructing and using content graphs for querybyexample speaker recognition tasks within a large speech corpus. This goal is achieved as follows: First, we describe an algorithm for constructing speaker content graphs, where nodes represent speech signals and edges represent speaker similarity. Speech signal similarity can be based on any standard vectorbased speaker comparison method, and the content graph can be constructed using an efficient incremental method for streaming data. Second, we apply random walk methods to the content graph to find matching examples to an unlabeled query set of speech signals. The contentgraph based method is contrasted to a more traditional approach that uses supervised training and stack detectors. Performance is compared in terms of information retrieval measures and computational complexity. The new contentgraph based method is shown to provide a promising lowcomplexity scalable alternative to standard speaker recognition methods. Index Terms: speaker recognition, graphs 1.
Adaptation of GraphBased SemiSupervised Methods to LargeScale Text Data
, 2011
"... Graphbased semisupervised learning methods have shown to be efficient and effective on network data by propagating labels along neighboring nodes. These methods can also be applied to general data by constructing a graph where the nodes are the instances and the edges are weighted by the similarit ..."
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Cited by 3 (2 self)
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Graphbased semisupervised learning methods have shown to be efficient and effective on network data by propagating labels along neighboring nodes. These methods can also be applied to general data by constructing a graph where the nodes are the instances and the edges are weighted by the similarity between feature vectors of instances. However, whereas a natural network is often sparse, a network of pairwise similarities between instances is dense, and prohibitively large for even moderately sized text datasets. We show, through using a simple general technique, how these learning methods can be exactly and efficiently applied to text data—using the complete pairwise similarity manifold—without resorting to sampling or sparsification. This technique also provides a unifying view of prior work on label propagation on text graphs, and we assess its effectiveness applied to two popular graphbased semisupervised methods on several large real datasets.
Collective Inference for Network Data with Copula Latent Markov Networks
"... The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. However, since Gaussian Markov networks model joint ..."
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Cited by 3 (0 self)
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The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. However, since Gaussian Markov networks model joint distributions over continuous label space, it is difficult to use them to reason about uncertainty in discrete labels. On the other hand, relational Markov networks model probability distributions over discrete label space, but since they condition on the graph structure, the marginal probability for an instance will vary based on the structure of the subnetwork observed around the instance. This implies that the marginals will not be identical across instances and can sometimes result in poor prediction performance. In this work, we propose a novel latent relational model based on copulas which allows use to make predictions in a discrete label space while ensuring identical marginals and at the same time incorporating some desirable properties of modeling relational dependencies in a continuous space. While copulas have recently been used for descriptive modeling, they have not been used for collective classification in large scale network data and the associated conditional inference problem has not been considered before. We develop an approximate inference algorithm, and demonstrate empirically that our proposed Copula Latent Markov Network models based on approximate inference outperform a number of competing relational classification models over a range of realworld relational classification tasks. 1.