Search Results for author:
Found 22 papers, 9 papers with code
Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors
Hyperbolic embedding improves embedding quality by exploiting the ever-expanding property of Hyperbolic space, but it also suffers from the same fate as box embedding as gradient descent like optimization is not simple in the Hyperbolic space.
Robust Node Representation Learning via Graph Variational Diffusion Networks
To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs.
Force-directed graph embedding with hops distance
In this paper, we propose a novel force-directed graph embedding method that utilizes the steady acceleration kinetic formula to embed nodes in a way that preserves graph topology and structural features.
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation
In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations.
Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns
Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences.
ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction
ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values.
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision
In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task.
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
Our proposed model synthesizes Gaussian mixture based latent representation over a deep generative model, such as GAN, for incremental detection of instances of emerging classes in the test data.
ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context
Semantic relationships, such as hyponym-hypernym, cause-effect, meronym-holonym etc.
Deperturbation of Online Social Networks via Bayesian Label Transition
However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task.
The Role of Graphlets in Viral Processes on Networks
We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution.
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction
Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources.
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes; Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node-pair and a dissimilar node-pair.
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes
However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way.
Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used in spectral clustering and community detection.
Name Disambiguation in Anonymized Graphs using Network Embedding
In real-world, our DNA is unique but many people share names.
Dis-S2V: Discourse Informed Sen2Vec
Our first approach retrofits (already trained) Sen2Vec vectors with respect to the network in two different ways: (1) using the adjacency relations of a node, and (2) using a stochastic sampling method which is more flexible in sampling neighbors of a node.
A large scale study of SVM based methods for abstract screening in systematic reviews
However, it is very hard to clearly understand the applicability of these methods in a systematic review platform because of the following challenges: (1) the use of non-overlapping metrics for the evaluation of the proposed methods, (2) usage of features that are very hard to collect, (3) using a small set of reviews for the evaluation, and (4) no solid statistical testing or equivalence grouping of the methods.
PRIIME: A Generic Framework for Interactive Personalized Interesting Pattern Discovery
The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns.
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.
Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection.
Feature Selection for Classification under Anonymity Constraint
Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously.
