73 research outputs found
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
Recent advances have shown the success of using reinforcement learning and
search to solve NP-hard graph-related tasks, such as Traveling Salesman
Optimization, Graph Edit Distance computation, etc. However, it remains unclear
how one can efficiently and accurately detect the occurrences of a small query
graph in a large target graph, which is a core operation in graph database
search, biomedical analysis, social group finding, etc. This task is called
Subgraph Matching which essentially performs subgraph isomorphism check between
a query graph and a large target graph. One promising approach to this
classical problem is the "learning-to-search" paradigm, where a reinforcement
learning (RL) agent is designed with a learned policy to guide a search
algorithm to quickly find the solution without any solved instances for
supervision. However, for the specific task of Subgraph Matching, though the
query graph is usually small given by the user as input, the target graph is
often orders-of-magnitude larger. It poses challenges to the neural network
design and can lead to solution and reward sparsity. In this paper, we propose
NSUBS with two innovations to tackle the challenges: (1) A novel
encoder-decoder neural network architecture to dynamically compute the matching
information between the query and the target graphs at each search state; (2) A
novel look-ahead loss function for training the policy network. Experiments on
six large real-world target graphs show that NSUBS can significantly improve
the subgraph matching performance
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs
Two-view knowledge graphs (KGs) jointly represent two components: an ontology
view for abstract and commonsense concepts, and an instance view for specific
entities that are instantiated from ontological concepts. As such, these KGs
contain heterogeneous structures that are hierarchical, from the ontology-view,
and cyclical, from the instance-view. Despite these various structures in KGs,
most recent works on embedding KGs assume that the entire KG belongs to only
one of the two views but not both simultaneously. For works that seek to put
both views of the KG together, the instance and ontology views are assumed to
belong to the same geometric space, such as all nodes embedded in the same
Euclidean space or non-Euclidean product space, an assumption no longer
reasonable for two-view KGs where different portions of the graph exhibit
different structures. To address this issue, we define and construct a
dual-geometric space embedding model (DGS) that models two-view KGs using a
complex non-Euclidean geometric space, by embedding different portions of the
KG in different geometric spaces. DGS utilizes the spherical space, hyperbolic
space, and their intersecting space in a unified framework for learning
embeddings. Furthermore, for the spherical space, we propose novel closed
spherical space operators that directly operate in the spherical space without
the need for mapping to an approximate tangent space. Experiments on public
datasets show that DGS significantly outperforms previous state-of-the-art
baseline models on KG completion tasks, demonstrating its ability to better
model heterogeneous structures in KGs
Fuzzy-Based Optimal Adaptive Line-of-Sight Path Following for Underactuated Unmanned Surface Vehicle with Uncertainties and Time-Varying Disturbances
This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of “explosion of complexity,” which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller
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