2 research outputs found
Graph Wasserstein Correlation Analysis for Movie Retrieval
Movie graphs play an important role to bridge heterogenous modalities of
videos and texts in human-centric retrieval. In this work, we propose Graph
Wasserstein Correlation Analysis (GWCA) to deal with the core issue therein,
i.e, cross heterogeneous graph comparison. Spectral graph filtering is
introduced to encode graph signals, which are then embedded as probability
distributions in a Wasserstein space, called graph Wasserstein metric learning.
Such a seamless integration of graph signal filtering together with metric
learning results in a surprise consistency on both learning processes, in which
the goal of metric learning is just to optimize signal filters or vice versa.
Further, we derive the solution of the graph comparison model as a classic
generalized eigenvalue decomposition problem, which has an exactly closed-form
solution. Finally, GWCA together with movie/text graphs generation are unified
into the framework of movie retrieval to evaluate our proposed method.
Extensive experiments on MovieGrpahs dataset demonstrate the effectiveness of
our GWCA as well as the entire framework
An Uncoupled Training Architecture for Large Graph Learning
Graph Convolutional Network (GCN) has been widely used in graph learning
tasks. However, GCN-based models (GCNs) is an inherently coupled training
framework repetitively conducting the complex neighboring aggregation, which
leads to the limitation of flexibility in processing large-scale graph. With
the depth of layers increases, the computational and memory cost of GCNs grow
explosively due to the recursive neighborhood expansion. To tackle these
issues, we present Node2Grids, a flexible uncoupled training framework that
leverages the independent mapped data for obtaining the embedding. Instead of
directly processing the coupled nodes as GCNs, Node2Grids supports a more
efficacious method in practice, mapping the coupled graph data into the
independent grid-like data which can be fed into the efficient Convolutional
Neural Network (CNN). This simple but valid strategy significantly saves memory
and computational resource while achieving comparable results with the leading
GCN-based models. Specifically, by ranking each node's influence through
degree, Node2Grids selects the most influential first-order as well as
second-order neighbors with central node fusion information to construct the
grid-like data. For further improving the efficiency of downstream tasks, a
simple CNN-based neural network is employed to capture the significant
information from the mapped grid-like data. Moreover, the grid-level attention
mechanism is implemented, which enables implicitly specifying the different
weights for neighboring nodes with different influences. In addition to the
typical transductive and inductive learning tasks, we also verify our framework
on million-scale graphs to demonstrate the superiority of the proposed
Node2Grids model against the state-of-the-art GCN-based approaches