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Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
DiffPool is proposed, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. Expand
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
TLDR
The experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models. Expand
Dynamic Network Embedding by Modeling Triadic Closure Process
TLDR
This paper presents a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network and can effectively be applied and help to identify telephone frauds in a mobile network, and to predict whether a user will repay her loans or not in a loan network. Expand
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
TLDR
This paper proposes a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. Expand
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding
TLDR
This paper proposes a novel embedding method to separately model “clean” and “noisy” mentions, and incorporates the given type hierarchy to induce loss functions. Expand
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
TLDR
A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other. Expand
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
TLDR
A global objective is formulated for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Expand
GraphRNN: A Deep Generative Model for Graphs
TLDR
The experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models. Expand
Mining Quality Phrases from Massive Text Corpora
TLDR
A new framework that extracts quality phrases from text corpora integrated with phrasal segmentation is proposed, which requires only limited training but the quality of phrases so generated is close to human judgment. Expand
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
TLDR
Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views. Expand
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