334 research outputs found
Heterogeneous graph learning for explainable recommendation over academic networks
With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach. Β© 2021 ACM
Structure fusion based on graph convolutional networks for semi-supervised classification
Suffering from the multi-view data diversity and complexity for
semi-supervised classification, most of existing graph convolutional networks
focus on the networks architecture construction or the salient graph structure
preservation, and ignore the the complete graph structure for semi-supervised
classification contribution. To mine the more complete distribution structure
from multi-view data with the consideration of the specificity and the
commonality, we propose structure fusion based on graph convolutional networks
(SF-GCN) for improving the performance of semi-supervised classification.
SF-GCN can not only retain the special characteristic of each view data by
spectral embedding, but also capture the common style of multi-view data by
distance metric between multi-graph structures. Suppose the linear relationship
between multi-graph structures, we can construct the optimization function of
structure fusion model by balancing the specificity loss and the commonality
loss. By solving this function, we can simultaneously obtain the fusion
spectral embedding from the multi-view data and the fusion structure as
adjacent matrix to input graph convolutional networks for semi-supervised
classification. Experiments demonstrate that the performance of SF-GCN
outperforms that of the state of the arts on three challenging datasets, which
are Cora,Citeseer and Pubmed in citation networks
Unsupervised Learning via Total Correlation Explanation
Learning by children and animals occurs effortlessly and largely without
obvious supervision. Successes in automating supervised learning have not
translated to the more ambiguous realm of unsupervised learning where goals and
labels are not provided. Barlow (1961) suggested that the signal that brains
leverage for unsupervised learning is dependence, or redundancy, in the sensory
environment. Dependence can be characterized using the information-theoretic
multivariate mutual information measure called total correlation. The principle
of Total Cor-relation Ex-planation (CorEx) is to learn representations of data
that "explain" as much dependence in the data as possible. We review some
manifestations of this principle along with successes in unsupervised learning
problems across diverse domains including human behavior, biology, and
language.Comment: Invited contribution for IJCAI 2017 Early Career Spotlight. 5 pages,
1 figur
COIN: Co-Cluster Infomax for Bipartite Graphs
Bipartite graphs are powerful data structures to model interactions between
two types of nodes, which have been used in a variety of applications, such as
recommender systems, information retrieval, and drug discovery. A fundamental
challenge for bipartite graphs is how to learn informative node embeddings.
Despite the success of recent self-supervised learning methods on bipartite
graphs, their objectives are discriminating instance-wise positive and negative
node pairs, which could contain cluster-level errors. In this paper, we
introduce a novel co-cluster infomax (COIN) framework, which captures the
cluster-level information by maximizing the mutual information of co-clusters.
Different from previous infomax methods which estimate mutual information by
neural networks, COIN could easily calculate mutual information. Besides, COIN
is an end-to-end coclustering method which can be trained jointly with other
objective functions and optimized via back-propagation. Furthermore, we also
provide theoretical analysis for COIN. We theoretically prove that COIN is able
to effectively increase the mutual information of node embeddings and COIN is
upper-bounded by the prior distributions of nodes. We extensively evaluate the
proposed COIN framework on various benchmark datasets and tasks to demonstrate
the effectiveness of COIN.Comment: NeurIPS 2022 GLFrontiers Worksho
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μ μΌλ‘ μμ°¨μ μΈ μλ£ κΈ°λ‘μ ν¨μΆν νμ ννκ³Ό λλΆμ΄ κ°μΈνλ μν μ§μμ ν¨μΆν ννμ ν¨κ» μ¬μ©νμ¬ ν₯ν μ§λ³ λ° μ§λ¨ μμΈ‘ λ¬Έμ μ νμ©νμλ€.This dissertation proposes a deep neural network-based medical concept and patient representation learning methods using medical claims data to solve two healthcare tasks, i.e., clinical outcome prediction and post-marketing adverse drug reaction (ADR) signal detection. First, we propose SAF-RNN, a Recurrent Neural Network (RNN)-based model that learns a deep patient representation based on the clinical sequences and patient characteristics. Our proposed model fuses different types of patient records using feature-based gating and self-attention. We demonstrate that high-level associations between two heterogeneous records are effectively extracted by our model, thus achieving state-of-the-art performances for predicting the risk probability of cardiovascular disease. Secondly, based on the observation that the distributed medical code embeddings represent temporal proximity between the medical codes, we introduce a graph structure to enhance the code embeddings with such temporal information. We construct a graph using the distributed code embeddings and the statistical information from the claims data. We then propose the Graph Neural Network(GNN)-based representation learning for post-marketing ADR detection. Our model shows competitive performances and provides valid ADR candidates. Finally, rather than using patient records alone, we utilize a knowledge graph to augment the patient representation with prior medical knowledge. Using SAF-RNN and GNN, the deep patient representation is learned from the clinical sequences and the personalized medical knowledge. It is then used to predict clinical outcomes, i.e., next diagnosis prediction and CVD risk prediction, resulting in state-of-the-art performances.1 Introduction 1
2 Background 8
2.1 Medical Concept Embedding 8
2.2 Encoding Sequential Information in Clinical Records 11
3 Deep Patient Representation with Heterogeneous Information 14
3.1 Related Work 16
3.2 Problem Statement 19
3.3 Method 20
3.3.1 RNN-based Disease Prediction Model 20
3.3.2 Self-Attentive Fusion (SAF) Encoder 23
3.4 Dataset and Experimental Setup 24
3.4.1 Dataset 24
3.4.2 Experimental Design 26
ii 3.4.3 Implementation Details 27
3.5 Experimental Results 28
3.5.1 Evaluation of CVD Prediction 28
3.5.2 Sensitivity Analysis 28
3.5.3 Ablation Studies 31
3.6 Further Investigation 32
3.6.1 Case Study: Patient-Centered Analysis 32
3.6.2 Data-Driven CVD Risk Factors 32
3.7 Conclusion 33
4 Graph-Enhanced Medical Concept Embedding 40
4.1 Related Work 42
4.2 Problem Statement 43
4.3 Method 44
4.3.1 Code Embedding Learning with Skip-gram Model 44
4.3.2 Drug-disease Graph Construction 45
4.3.3 A GNN-based Method for Learning Graph Structure 47
4.4 Dataset and Experimental Setup 49
4.4.1 Dataset 49
4.4.2 Experimental Design 50
4.4.3 Implementation Details 52
4.5 Experimental Results 53
4.5.1 Evaluation of ADR Detection 53
4.5.2 Newly-Described ADR Candidates 54
4.6 Conclusion 55
5 Knowledge-Augmented Deep Patient Representation 57
5.1 Related Work 60
5.1.1 Incorporating Prior Medical Knowledge for Clinical Outcome Prediction 60
5.1.2 Inductive KGC based on Subgraph Learning 61
5.2 Method 61
5.2.1 Extracting Personalized KG 61
5.2.2 KA-SAF: Knowledge-Augmented Self-Attentive Fusion Encoder 64
5.2.3 KGC as a Pre-training Task 68
5.2.4 Subgraph Infomax: SGI 69
5.3 Dataset and Experimental Setup 72
5.3.1 Clinical Outcome Prediction 72
5.3.2 Next Diagnosis Prediction 72
5.4 Experimental Results 73
5.4.1 Cardiovascular Disease Prediction 73
5.4.2 Next Diagnosis Prediction 73
5.4.3 KGC on SemMed KG 73
5.5 Conclusion 74
6 Conclusion 77
Abstract (In Korean) 90
Acknowlegement 92λ°
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