42 research outputs found
Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict user actions
based on anonymous sessions. Previous methods model a session as a sequence and
estimate user representations besides item representations to make
recommendations. Though achieved promising results, they are insufficient to
obtain accurate user vectors in sessions and neglect complex transitions of
items. To obtain accurate item embedding and take complex transitions of items
into account, we propose a novel method, i.e. Session-based Recommendation with
Graph Neural Networks, SR-GNN for brevity. In the proposed method, session
sequences are modeled as graph-structured data. Based on the session graph, GNN
can capture complex transitions of items, which are difficult to be revealed by
previous conventional sequential methods. Each session is then represented as
the composition of the global preference and the current interest of that
session using an attention network. Extensive experiments conducted on two real
datasets show that SR-GNN evidently outperforms the state-of-the-art
session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial
Intelligence (AAAI-19
A Systematic Survey of Chemical Pre-trained Models
Deep learning has achieved remarkable success in learning representations for
molecules, which is crucial for various biochemical applications, ranging from
property prediction to drug design. However, training Deep Neural Networks
(DNNs) from scratch often requires abundant labeled molecules, which are
expensive to acquire in the real world. To alleviate this issue, tremendous
efforts have been devoted to Molecular Pre-trained Models (CPMs), where DNNs
are pre-trained using large-scale unlabeled molecular databases and then
fine-tuned over specific downstream tasks. Despite the prosperity, there lacks
a systematic review of this fast-growing field. In this paper, we present the
first survey that summarizes the current progress of CPMs. We first highlight
the limitations of training molecular representation models from scratch to
motivate CPM studies. Next, we systematically review recent advances on this
topic from several key perspectives, including molecular descriptors, encoder
architectures, pre-training strategies, and applications. We also highlight the
challenges and promising avenues for future research, providing a useful
resource for both machine learning and scientific communities.Comment: IJCAI 2023, Survey Trac
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Multimodal brain networks characterize complex connectivities among different
brain regions from both structural and functional aspects and provide a new
means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have
become a de facto model for analyzing graph-structured data. However, how to
employ GNNs to extract effective representations from brain networks in
multiple modalities remains rarely explored. Moreover, as brain networks
provide no initial node features, how to design informative node attributes and
leverage edge weights for GNNs to learn is left unsolved. To this end, we
develop a novel multiview GNN for multimodal brain networks. In particular, we
regard each modality as a view for brain networks and employ contrastive
learning for multimodal fusion. Then, we propose a GNN model which takes
advantage of the message passing scheme by propagating messages based on degree
statistics and brain region connectivities. Extensive experiments on two
real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of
our proposed method over state-of-the-art baselines.Comment: Accepted to ICML 2021 Workshop on Computational Approaches to Mental
Healt
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Session-based recommendation nowadays plays a vital role in many websites,
which aims to predict users' actions based on anonymous sessions. There have
emerged many studies that model a session as a sequence or a graph via
investigating temporal transitions of items in a session. However, these
methods compress a session into one fixed representation vector without
considering the target items to be predicted. The fixed vector will restrict
the representation ability of the recommender model, considering the diversity
of target items and users' interests. In this paper, we propose a novel target
attentive graph neural network (TAGNN) model for session-based recommendation.
In TAGNN, target-aware attention adaptively activates different user interests
with respect to varied target items. The learned interest representation vector
varies with different target items, greatly improving the expressiveness of the
model. Moreover, TAGNN harnesses the power of graph neural networks to capture
rich item transitions in sessions. Comprehensive experiments conducted on
real-world datasets demonstrate its superiority over state-of-the-art methods.Comment: 5 pages, accepted to SIGIR 2020, authors' versio
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive
unlabeled data, has become a prominent paradigm to solve a variety of tasks in
computational chemistry and drug discovery. Recently, prosperous progress has
been made in molecular pretraining with different molecular featurizations,
including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of
molecular featurizations with their corresponding neural architectures in
molecular pretraining remains largely unexamined. In this paper, through two
case studies -- chirality classification and aromatic ring counting -- we first
demonstrate that different featurization techniques convey chemical information
differently. In light of this observation, we propose a simple and effective
MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO
comprehensively leverages multiple featurizations that complement each other
and outperforms existing state-of-the-art models that solely relies on one or
two featurizations on a wide range of molecular property prediction tasks.Comment: 24 pages, work in progres
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Human brains lie at the core of complex neurobiological systems, where the
neurons, circuits, and subsystems interact in enigmatic ways. Understanding the
structural and functional mechanisms of the brain has long been an intriguing
pursuit for neuroscience research and clinical disorder therapy. Mapping the
connections of the human brain as a network is one of the most pervasive
paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged
as a potential method for modeling complex network data. Deep models, on the
other hand, have low interpretability, which prevents their usage in
decision-critical contexts like healthcare. To bridge this gap, we propose an
interpretable framework to analyze disorder-specific Regions of Interest (ROIs)
and prominent connections. The proposed framework consists of two modules: a
brain-network-oriented backbone model for disease prediction and a globally
shared explanation generator that highlights disorder-specific biomarkers
including salient ROIs and important connections. We conduct experiments on
three real-world datasets of brain disorders. The results verify that our
framework can obtain outstanding performance and also identify meaningful
biomarkers. All code for this work is available at
https://github.com/HennyJie/IBGNN.git.Comment: Previous version presented at icml-imlh 2021 (no proceedings,
archived at 2107.05097), this version is accepted to miccai 202