3 research outputs found
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
Given multiple input signals, how can we infer node importance in a knowledge
graph (KG)? Node importance estimation is a crucial and challenging task that
can benefit a lot of applications including recommendation, search, and query
disambiguation. A key challenge towards this goal is how to effectively use
input from different sources. On the one hand, a KG is a rich source of
information, with multiple types of nodes and edges. On the other hand, there
are external input signals, such as the number of votes or pageviews, which can
directly tell us about the importance of entities in a KG. While several
methods have been developed to tackle this problem, their use of these external
signals has been limited as they are not designed to consider multiple signals
simultaneously. In this paper, we develop an end-to-end model MultiImport,
which infers latent node importance from multiple, potentially overlapping,
input signals. MultiImport is a latent variable model that captures the
relation between node importance and input signals, and effectively learns from
multiple signals with potential conflicts. Also, MultiImport provides an
effective estimator based on attentive graph neural networks. We ran
experiments on real-world KGs to show that MultiImport handles several
challenges involved with inferring node importance from multiple input signals,
and consistently outperforms existing methods, achieving up to 23.7% higher
NDCG@100 than the state-of-the-art method.Comment: KDD 2020 Research Track. 10 page
Randomized Algorithms for Computation of Tucker decomposition and Higher Order SVD (HOSVD)
Big data analysis has become a crucial part of new emerging technologies such
as the internet of things, cyber-physical analysis, deep learning, anomaly
detection, etc. Among many other techniques, dimensionality reduction plays a
key role in such analyses and facilitates feature selection and feature
extraction. Randomized algorithms are efficient tools for handling big data
tensors. They accelerate decomposing large-scale data tensors by reducing the
computational complexity of deterministic algorithms and the communication
among different levels of the memory hierarchy, which is the main bottleneck in
modern computing environments and architectures. In this paper, we review
recent advances in randomization for the computation of Tucker decomposition
and Higher Order SVD (HOSVD). We discuss random projection and sampling
approaches, single-pass, and multi-pass randomized algorithms, and how to
utilize them in the computation of the Tucker decomposition and the HOSVD.
Simulations on synthetic and real datasets are provided to compare the
performance of some of the best and most promising algorithms