Search Results for author:
Found 19 papers, 10 papers with code
The Forward-Backward Embedding of Directed Graphs
In particular, two nodes having many common successors in the graph tend to be represented by close vectors in the embedding space.
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning
The task of semi-supervised classification aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds.
YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy
Knowledge Bases (KBs) find applications in many knowledge-intensive tasks and, most notably, in information retrieval.
A Self-Encoder for Learning Nearest Neighbors
We present the self-encoder, a neural network trained to guess the identity of each data sample.
KNNs of Semantic Encodings for Rating Prediction
This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction.
Pairwise Adjusted Mutual Information
A well-known metric for quantifying the similarity between two clusterings is the adjusted mutual information.
A Consistent Diffusion-Based Algorithm for Semi-Supervised Classification on Graphs
Semi-supervised classification on graphs aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds.
Time Series Source Separation with Slow Flows
In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible neural latent variable models.
Spectral embedding of regularized block models
Spectral embedding is a popular technique for the representation of graph data.
WikiDataSets: Standardized sub-graphs from Wikidata
Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets.
Weighted Spectral Embedding of Graphs
We present a novel spectral embedding of graphs that incorporates weights assigned to the nodes, quantifying their relative importance.
Learning Graph Representations by Dendrograms
Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks.
Hierarchical Graph Clustering using Node Pair Sampling
We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques.
A Streaming Algorithm for Graph Clustering
We introduce a novel algorithm to perform graph clustering in the edge streaming setting.
Improving PageRank for Local Community Detection
Community detection is a classical problem in the field of graph mining.
Social and Information Networks Physics and Society
A Minimax Optimal Algorithm for Crowdsourcing
We further propose Triangular Estimation (TE), an algorithm for estimating the reliability of workers.
A Streaming Algorithm for Crowdsourced Data Classification
We propose a streaming algorithm for the binary classification of data based on crowdsourcing.
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
This paper presents a novel spectral algorithm with additive clustering designed to identify overlapping communities in networks.
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards
This two-target algorithm achieves a long-term average regret in $\sqrt{2n}$ for a large parameter $m$ and a known time horizon $n$.
