141,161 research outputs found
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
With the increase of content pages and interactive buttons in online services
such as online-shopping and video-watching websites, industrial-scale
recommender systems face challenges in multi-domain and multi-task
recommendations. The core of multi-task and multi-domain recommendation is to
accurately capture user interests in multiple scenarios given multiple user
behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter
and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work
(\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes
personalized prior information as input and dynamically scales the bottom-level
Embedding and top-level DNN hidden units through gate mechanisms.
\textit{Embedding Personalized Network (EPNet)} performs personalized selection
on Embedding to fuse features with different importance for different users in
multiple domains. \textit{Parameter Personalized Network (PPNet)} executes
personalized modification on DNN parameters to balance targets with different
sparsity for different users in multiple tasks. We have made a series of
special engineering optimizations combining the Kuaishou training framework and
the online deployment environment. By infusing personalized selection of
Embedding and personalized modification of DNN parameters, PEPNet tailored to
the interests of each individual obtains significant performance gains, with
online improvements exceeding 1\% in multiple task metrics across multiple
domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million
users every day.Comment: Accepted by KDD 202
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Network analysis of human brain connectivity is critically important for
understanding brain function and disease states. Embedding a brain network as a
whole graph instance into a meaningful low-dimensional representation can be
used to investigate disease mechanisms and inform therapeutic interventions.
Moreover, by exploiting information from multiple neuroimaging modalities or
views, we are able to obtain an embedding that is more useful than the
embedding learned from an individual view. Therefore, multi-view multi-graph
embedding becomes a crucial task. Currently, only a few studies have been
devoted to this topic, and most of them focus on the vector-based strategy
which will cause structural information contained in the original graphs lost.
As a novel attempt to tackle this problem, we propose Multi-view Multi-graph
Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric
tensors and using tensor techniques to simultaneously leverage the dependencies
and correlations among multi-view and multi-graph brain networks. Extensive
experiments on real HIV and bipolar disorder brain network datasets demonstrate
the superior performance of M2E on clustering brain networks by leveraging the
multi-view multi-graph interactions
- …