摘要 :
We address the problem of modeling bursty diffusion of text-based events over a social network of user nodes. The purpose is to recover, disentangle and analyze overlapping social conversations from the perspective of user-topic p...
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We address the problem of modeling bursty diffusion of text-based events over a social network of user nodes. The purpose is to recover, disentangle and analyze overlapping social conversations from the perspective of user-topic preferences, user-user connection strengths and, importantly, topic transitions. For this, we propose a Dual-Network Hawkes Process (DNHP), which executes over a graph whose nodes are user-topic pairs, and closeness of nodes is captured using topic-topic, a user-user, and user-topic interactions. No existing Hawkes Process model captures such multiple interactions simultaneously. Additionally, unlike existing Hawkes Process based models, where event times are generated first, and event topics are conditioned on the event times, the DNHP is more faithful to the underlying social process by making the event times depend on interacting (user, topic) pairs. We develop a Gibbs sampling algorithm for estimating the three network parameters that allows evidence to flow between the parameter spaces. Using experiments over large real collection of tweets by US politicians, we show that the DNHP generalizes better than state of the art models, and also provides interesting insights about user and topic transitions.
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摘要 :
We address the problem of modeling bursty diffusion of text-based events over a social network of user nodes. The purpose is to recover, disentangle and analyze overlapping social conversations from the perspective of user-topic p...
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We address the problem of modeling bursty diffusion of text-based events over a social network of user nodes. The purpose is to recover, disentangle and analyze overlapping social conversations from the perspective of user-topic preferences, user-user connection strengths and, importantly, topic transitions. For this, we propose a Dual-Network Hawkes Process (DNHP), which executes over a graph whose nodes are user-topic pairs, and closeness of nodes is captured using topic-topic, a user-user, and user-topic interactions. No existing Hawkes Process model captures such multiple interactions simultaneously. Additionally, unlike existing Hawkes Process based models, where event times are generated first, and event topics are conditioned on the event times, the DNHP is more faithful to the underlying social process by making the event times depend on interacting (user, topic) pairs. We develop a Gibbs sampling algorithm for estimating the three network parameters that allows evidence to flow between the parameter spaces. Using experiments over large real collection of tweets by US politicians, we show that the DNHP generalizes better than state of the art models, and also provides interesting insights about user and topic transitions.
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Distance parameters are extensively used to design efficient algorithms for many hard graph problems. They measure how far a graph is from belonging to some class of graphs. If a problem is tractable on a class of graphs F, then d...
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Distance parameters are extensively used to design efficient algorithms for many hard graph problems. They measure how far a graph is from belonging to some class of graphs. If a problem is tractable on a class of graphs F, then distances to F provide interesting parameterizations to that problem. For example, the parameter vertex cover measures the closeness of a graph to an edgeless graph. Many hard problems are tractable on graphs of small vertex cover. However, the parameter vertex cover is very restrictive in the sense that the class of graphs with bounded vertex cover is small. This significantly limits its usefulness in practical applications. In general, it is desirable to find tractable results for parameters such that the class of graphs with the parameter bounded should be as large as possible. In this spirit, we consider the parameter distance to threshold graphs, which are graphs that are both split graphs and cographs. It is a natural choice of an intermediate parameter between vertex cover and clique-width. In this paper, we give parameterized algorithms for some hard graph problems parameterized by the distance to threshold graphs. We show that HAPPY COLORING and EMPIRE COLORING problems are fixed-parameter tractable when parameterized by the distance to threshold graphs. We also present an approximation algorithm to compute the BOXICITY of a graph parameterized by the distance to threshold graphs.
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摘要 :
Distance parameters are extensively used to design efficient algorithms for many hard graph problems. They measure how far a graph is from belonging to some class of graphs. If a problem is tractable on a class of graphs then dist...
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Distance parameters are extensively used to design efficient algorithms for many hard graph problems. They measure how far a graph is from belonging to some class of graphs. If a problem is tractable on a class of graphs then distances to j provide interesting parameterizations to that problem. For example, the parameter vertex cover measures the closeness of a graph to an edgeless graph. Many hard problems are tractable on graphs of small vertex cover. However, the parameter vertex cover is very restrictive in the sense that the class of graphs with bounded vertex cover is small. This significantly limits its usefulness in practical applications. In general, it is desirable to find tractable results for parameters such that the class of graphs with the parameter bounded should be as large as possible. In this spirit, we consider the parameter distance to threshold graphs, which are graphs that are both split graphs and cographs. It is a natural choice of an intermediate parameter between vertex cover and clique-width. In this paper, we give parameterized algorithms for some hard graph problems parameterized by the distance to threshold graphs. We show that Happy Coloring and Empire Coloring problems are fixed-parameter tractable when parameterized by the distance to threshold graphs. We also present an approximation algorithm to compute the Boxicity of a graph parameterized by the distance to threshold graphs.
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Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of ...
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Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of n vectors, each in R~d, we present algorithms to select a sub-linear number of these vectors as coreset, while guaranteeing that the CP decomposition of the p-moment tensor of the coreset approximates the corresponding decomposition of the p-moment tensor computed from the full data. We introduce two novel algorithmic techniques: online filtering and kernelization. Using these two, we present four algorithms that achieve different tradeoffs of coreset size, update time and working space, beating or matching various state of the art algorithms. In the case of matrices (2-ordered tensor), our online row sampling algorithm guarantees (1 ± ε) relative error spectral approximation. We show applications of our algorithms in learning single topic modeling.
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摘要 :
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns be...
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Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with user-user and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topic-topic interactions. We show using experiments on real and semi-synthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do.
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摘要 :
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns be...
展开
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with user-user and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topic-topic interactions. We show using experiments on real and semisynthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do.
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摘要 :
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns be...
展开
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with user-user and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topic-topic interactions. We show using experiments on real and semi-synthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do.
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Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority ...
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Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of these are text-based messaging platforms. Development of Chatbots that automatically understand latent emotions in the textual message is a challenging task. In this paper, we present an automatic emotion detection system that aims to detect the emotion of a person textually conversing with a chatbot. We explore deep learning techniques such as CNN and LSTM based neural networks and outperformed the baseline score by 14%. The trained model and code are kept in public domain.
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摘要 :
Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority ...
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Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of these are text-based messaging platforms. Development of Chatbots that automatically understand latent emotions in the textual message is a challenging task. In this paper, we present an automatic emotion detection system that aims to detect the emotion of a person textually conversing with a chatbot. We explore deep learning techniques such as CNN and LSTM based neural networks and outperformed the baseline score by 14%. The trained model and code are kept in public domain.
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