Search Results for author:
Found 22 papers, 5 papers with code
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
PersonaSAGE: A Multi-Persona Graph Neural Network
Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings.
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.
Graph Learning with Localized Neighborhood Fairness
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
Developing a Conversational Recommendation System for Navigating Limited Options
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.
Insight-centric Visualization Recommendation
This global ranking makes it difficult and time-consuming for users to find the most interesting or relevant insights.
Personalized Visualization Recommendation
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback.
Heterogeneous Graphlets
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5. 92 from a 7-point Likert scale compared to only 3. 45).
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles.
Figure Captioning with Reasoning and Sequence-Level Training
In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure.
Dynamic Node Embeddings from Edge Streams
In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks.
Heterogeneous Network Motifs
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
Latent Network Summarization: Bridging Network Embedding and Summarization
Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.
Social and Information Networks
Higher-order Spectral Clustering for Heterogeneous Graphs
Using typed-graphlets as a basis, we develop a general principled framework for higher-order clustering in heterogeneous networks.
Higher-order Graph Convolutional Networks
Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
Attention Models in Graphs: A Survey
However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.
HONE: Higher-Order Network Embeddings
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs.
