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View article: ActUp:Analyzing and Consolidating tSNE & UMAP
ActUp:Analyzing and Consolidating tSNE & UMAP Open
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has been done to study their full span of differences. We theoret…
View article: ActUp: Analyzing and Consolidating tSNE and UMAP
ActUp: Analyzing and Consolidating tSNE and UMAP Open
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has been done to study their full span of differences. We theoret…
View article: Workload-Aware Materialization of Junction Trees
Workload-Aware Materialization of Junction Trees Open
Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answering probabilistic queries over a subset of variables in t…
View article: Learning Ideological Embeddings from Information Cascades
Learning Ideological Embeddings from Information Cascades Open
Modeling information cascades in a social network through the lenses of the\nideological leaning of its users can help understanding phenomena such as\nmisinformation propagation and confirmation bias, and devising techniques for\nmitigati…
View article: Workload-Aware Materialization of Junction Trees
Workload-Aware Materialization of Junction Trees Open
Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answering probabilistic queries over a subset of variables in t…
View article: Efficient and Effective Algorithms for Revenue Maximization in Social Advertising
Efficient and Effective Algorithms for Revenue Maximization in Social Advertising Open
We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owne…
View article: Efficient and Effective Algorithms for Revenue Maximization in Social Advertising
Efficient and Effective Algorithms for Revenue Maximization in Social Advertising Open
We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owne…
View article: Workload-aware Materialization for Efficient Variable Elimination on Bayesian Networks
Workload-aware Materialization for Efficient Variable Elimination on Bayesian Networks Open
Bayesian networks are general, well-studied probabilistic models that capture dependencies among a set of variables. Variable Elimination is a fundamental algorithm for probabilistic inference over Bayesian networks. In this paper, we prop…
View article: Maximizing the Diversity of Exposure in a Social Network
Maximizing the Diversity of Exposure in a Social Network Open
| openaire: EC/H2020/654024/EU//SoBigData
View article: Co-exposure Maximization in Online Social Networks
Co-exposure Maximization in Online Social Networks Open
Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse. However, with its algorithmically-curated and virally-propagating content, social media has contributed further to …
View article: Query the model: precomputations for efficient inference with Bayesian Networks
Query the model: precomputations for efficient inference with Bayesian Networks Open
Variable Elimination is a fundamental algorithm for probabilistic inference over Bayesian networks. In this paper, we propose a novel materialization method for Variable Elimination, which can lead to significant efficiency gains when answ…
View article: Discovering interesting cycles in graphs
Discovering interesting cycles in graphs Open
Cycles in graphs often signify interesting processes. For example, cyclic trading patterns can indicate inefficiencies or economic dependencies in trade networks, cycles in food webs can identify fragile dependencies in ecosystems, and cyc…
View article: Robust Cascade Reconstruction by Steiner Tree Sampling
Robust Cascade Reconstruction by Steiner Tree Sampling Open
| openaire: EC/H2020/654024/EU//SoBigData
View article: Maximizing the Diversity of Exposure in a Social Network
Maximizing the Diversity of Exposure in a Social Network Open
Social-media platforms have created new ways for citizens to stay informed\nand participate in public debates. However, to enable a healthy environment for\ninformation sharing, social deliberation, and opinion formation, citizens need\nto…
View article: Mining Frequent Patterns in Evolving Graphs
Mining Frequent Patterns in Evolving Graphs Open
Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to find all the k-vertex subgraphs that appear with frequency greater than a given threshold. FSM has numerous appli- cations ranging from biology to network science, a…
View article: Robust Cascade Reconstruction by Steiner Tree Sampling
Robust Cascade Reconstruction by Steiner Tree Sampling Open
We consider a network where an infection cascade has taken place and a subset of infected nodes has been partially observed. Our goal is to reconstruct the underlying cascade that is likely to have generated these observations. We reduce t…
View article: Mining Frequent Patterns in Evolving Graphs
Mining Frequent Patterns in Evolving Graphs Open
Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to find all the $k$-vertex subgraphs that appear with frequency greater than a given threshold. FSM has numerous applications ranging from biology to network science, a…
View article: Revenue Maximization in Incentivized Social Advertising
Revenue Maximization in Incentivized Social Advertising Open
Incentivized social advertising, an emerging marketing model, provides monetization opportunities not only to the owners of the social networking platforms but also to their influential users by offering a "cut" on the advertising revenue.…