252 Result(s)
within
Algorithm Analysis and Problem Complexity
Computer Communication Networks
Data Mining and Knowledge Discovery
**mo Wang
Chapter
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Chapter and Conference Paper
Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining. Recently, pre-trained language models (e.g., BERT) yield state-of-the-art results and dominate...
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Chapter and Conference Paper
PhotoStylist: Altering the Style of Photos Based on the Connotations of Texts
The need to modify a photo to reflect the connotations of a text can arise due to multifarious reasons (e.g., a musician might modify a photo in the album cover to better reflect the connotations in her song l...
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Chapter and Conference Paper
AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life
Remaining Useful Life (RUL) prediction of equipment can estimate the time when equipment reaches the safe operating limit, which is essential for strategy formulation to reduce the possibility of loss due to u...
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Chapter and Conference Paper
Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-Hailing Demand Forecasting
Accurately predicting the demand for ride-hailing in the region is important for transportation and the economy. Prior works are devoted to mining the spatio-temporal correlations between regions limited to hi...
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Chapter and Conference Paper
Incrementally Finding the Vertices Absent from the Maximum Independent Sets
A vertex v in a graph G is called an absent vertex if it is not in any maximum independent set of G. Absent vertex discovery is useful in various scenarios. For example, if G depicts a wireless communication inte...
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Chapter and Conference Paper
RAGA: Relation-Aware Graph Attention Networks for Global Entity Alignment
Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majorit...
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Chapter and Conference Paper
Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data
Mining anomalous objects from multi-view data is a challenging issue as data collected from diverse sources have more complicated distributions and exhibit inconsistently heterogeneous properties. Existing mul...
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Chapter and Conference Paper
A Proximity Forest for Multivariate Time Series Classification
Multivariate time series (MTS) classification has gained attention in recent years with the increase of multiple temporal datasets from various domains, such as human activity recognition, medical diagnosis, e...
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Chapter and Conference Paper
A Meta-path Based Graph Convolutional Network with Multi-scale Semantic Extractions for Heterogeneous Event Classification
Heterogeneous social events modeling in large and noisy data sources is an important task for applications such as international situation assessment and disaster relief. Accurate and interpretable classificat...
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Chapter and Conference Paper
GLAD-PAW: Graph-Based Log Anomaly Detection by Position Aware Weighted Graph Attention Network
Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas. In this work, we focus on log data (especially computer system logs) which is a valuable source to in...
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Chapter and Conference Paper
Weak Supervision Network Embedding for Constrained Graph Learning
Constrained learning, a weakly supervised learning task, aims to incorporate domain constraints to learn models without requiring labels for each instance. Because weak supervision knowledge is useful and easy...
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Chapter and Conference Paper
A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations
Identifying potential causal genes for disease phenotypes is essential for disease treatment and facilitates drug development. Inspired by existing random-walk based embedding methods and the hierarchical stru...
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Chapter and Conference Paper
Lifelong Learning Based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of perform...
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Chapter and Conference Paper
Adaptive Graph Co-Attention Networks for Traffic Forecasting
Traffic forecasting has remained a challenging topic in the field of transportation, due to the time-varying traffic patterns and complicated spatial dependencies on road networks. To address such challenges, ...
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Chapter and Conference Paper
Content Matters: A GNN-Based Model Combined with Text Semantics for Social Network Cascade Prediction
Effectively modeling and predicting the size of information cascades is essential for downstream tasks such as rumor detection and epidemic prevention. Traditional methods normally rely on tedious hand-crafted...
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Chapter and Conference Paper
Extending Graph Pattern Matching with Regular Expressions
Graph pattern matching, which is to compute the set M(Q, G) of matches of Q in G, for the given pattern graph Q and data graph G, has been increasingly used in emerging applications e.g., social network analysis....
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Chapter and Conference Paper
Bounded Pattern Matching Using Views
Bounded evaluation using views is to compute the answers \(Q(\mathcal{D})\) to a query Q in a dataset ...
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Chapter and Conference Paper
KPML: A Novel Probabilistic Perspective Kernel Mahalanobis Distance Metric Learning Model for Semi-supervised Clustering
Metric learning aims to transform features of data into another based on some given distance relationships, which may improve the performances of distance-based machine learning models. Most existing methods u...
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Chapter and Conference Paper
View Selection for Graph Pattern Matching
View-based techniques have been investigated on relational data, XML and graphs and proven effective for querying big data. While the pivot of using materialized views for query answering is view selection. Th...
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Chapter and Conference Paper
Using Deep Neural Network to Predict Drug Sensitivity of Cancer Cell Lines
High-throughput screening technology has provided a large amount of drug sensitivity data for hundreds of compounds on cancer cell lines. In this study, we have developed a deep learning architecture based on...
