1,109 research outputs found
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks. MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks. MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels. MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates. We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM). For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%. For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is a critical task for many industrial
systems, such as display advertising and recommender systems. Recently,
modeling user behavior sequences attracts much attention and shows great
improvements in the CTR field. Existing works mainly exploit attention
mechanism based on embedding product when considering relations between user
behaviors and target item. However, this methodology lacks of concrete
semantics and overlooks the underlying reasons driving a user to click on a
target item. In this paper, we propose a new framework named Multiplex
Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex
relations between user behaviors and target item to enhance CTR prediction.
Multiplex relations consist of meaningful semantics, which can bring a better
understanding on users' interests from different perspectives. To explore and
model multiplex relations, we propose to incorporate various graphs (e.g.,
knowledge graph and item-item similarity graph) to construct multiple
relational paths between user behaviors and target item. Then Bi-LSTM is
applied to encode each path in the path extractor layer. A path fusion network
and a path activation network are devised to adaptively aggregate and finally
learn the representation of all paths for CTR prediction. Extensive offline and
online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202
- …