9 research outputs found
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related
problems such as node classification and graph classification, where the
superior performance is mainly established when natural node features are
available. However, it is not well understood how GNNs work without natural
node features, especially regarding the various ways to construct artificial
ones. In this paper, we point out the two types of artificial node
features,i.e., positional and structural node features, and provide insights on
why each of them is more appropriate for certain tasks,i.e., positional node
classification, structural node classification, and graph classification.
Extensive experimental results on 10 benchmark datasets validate our insights,
thus leading to a practical guideline on the choices between different
artificial node features for GNNs on non-attributed graphs. The code is
available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on
Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
Spider webs are incredible biological structures, comprising thin but strong
silk filament and arranged into complex hierarchical architectures with
striking mechanical properties (e.g., lightweight but high strength, achieving
diverse mechanical responses). While simple 2D orb webs can easily be mimicked,
the modeling and synthesis of 3D-based web structures remain challenging,
partly due to the rich set of design features. Here we provide a detailed
analysis of the heterogenous graph structures of spider webs, and use deep
learning as a way to model and then synthesize artificial, bio-inspired 3D web
structures. The generative AI models are conditioned based on key geometric
parameters (including average edge length, number of nodes, average node
degree, and others). To identify graph construction principles, we use
inductive representation sampling of large experimentally determined spider web
graphs, to yield a dataset that is used to train three conditional generative
models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics,
with sparse neighbor representation, 2) a discrete diffusion model with full
neighbor representation, and 3) an autoregressive transformer architecture with
full neighbor representation. All three models are scalable, produce complex,
de novo bio-inspired spider web mimics, and successfully construct graphs that
meet the design objectives. We further propose algorithm that assembles web
samples produced by the generative models into larger-scale structures based on
a series of geometric design targets, including helical and parametric shapes,
mimicking, and extending natural design principles towards integration with
diverging engineering objectives. Several webs are manufactured using 3D
printing and tested to assess mechanical properties
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to its
wide range of applications, generative models for graphs have a rich history,
which, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for the graph generation. Firstly, the formal definition of
deep generative models for the graph generation as well as preliminary
knowledge is provided. Secondly, two taxonomies of deep generative models for
unconditional, and conditional graph generation respectively are proposed; the
existing works of each are compared and analyzed. After that, an overview of
the evaluation metrics in this specific domain is provided. Finally, the
applications that deep graph generation enables are summarized and five
promising future research directions are highlighted
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines