7 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
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
Broad Learning for Healthcare
A broad spectrum of data from different modalities are generated in the
healthcare domain every day, including scalar data (e.g., clinical measures
collected at hospitals), tensor data (e.g., neuroimages analyzed by research
institutes), graph data (e.g., brain connectivity networks), and sequence data
(e.g., digital footprints recorded on smart sensors). Capability for modeling
information from these heterogeneous data sources is potentially transformative
for investigating disease mechanisms and for informing therapeutic
interventions.
Our works in this thesis attempt to facilitate healthcare applications in the
setting of broad learning which focuses on fusing heterogeneous data sources
for a variety of synergistic knowledge discovery and machine learning tasks. We
are generally interested in computer-aided diagnosis, precision medicine, and
mobile health by creating accurate user profiles which include important
biomarkers, brain connectivity patterns, and latent representations. In
particular, our works involve four different data mining problems with
application to the healthcare domain: multi-view feature selection, subgraph
pattern mining, brain network embedding, and multi-view sequence prediction.Comment: PhD Thesis, University of Illinois at Chicago, March 201
A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods
Information Networks are collections of data structures that are used to model interactions in social and living phenomena. They can be either homogeneous or heterogeneous and static or dynamic depending upon the type and nature of relations between the network entities. Static, homogeneous and heterogenous networks have been widely studied in data mining but recently, there has been renewed interest in dynamic heterogeneous information networks (DHIN) analysis because the rich temporal, structural and semantic information is hidden in this kind of network. The heterogeneity and dynamicity of the real-time networks offer plenty of prospects as well as a lot of challenges for data mining. There has been substantial research undertaken on the exploration of entities and their link identification in heterogeneous networks. However, the work on the formal construction and change mining of heterogeneous information networks is still infant due to its complex structure and rich semantics. Researchers have used clusters-based methods and frequent pattern-mining techniques in the past for change discovery in dynamic heterogeneous networks. These methods only work on small datasets, only provide the structural change discovery and fail to consider the quick and parallel process on big data. The problem with these methods is also that cluster-based approaches provide the structural changes while the pattern-mining provide semantic characteristics of changes in a dynamic network. Another interesting but challenging problem that has not been considered by past studies is to extract knowledge from these semantically richer networks based on the user-specific constraint.This study aims to develop a new change mining system ChaMining to investigate dynamic heterogeneous network data, using knowledge engineering with semantic web technologies and data mining to overcome the problems of previous techniques, this system and approach are important in academia as well as real-life applications to support decision-making based on temporal network data patterns. This research has designed a novel framework “ChaMining” (i) to find relational patterns in dynamic networks locally and globally by employing domain ontologies (ii) extract knowledge from these semantically richer networks based on the user-specific (meta-paths) constraints (iii) Cluster the relational data patterns based on structural properties of nodes in the dynamic network (iv) Develop a hybrid approach using knowledge engineering, temporal rule mining and clustering to detect changes in the dynamic heterogeneous networks.The evidence is presented in this research shows that the proposed framework and methods work very efficiently on the benchmark big dynamic heterogeneous datasets. The empirical results can contribute to a better understanding of the rich semantics of DHIN and how to mine them using the proposed hybrid approach. The proposed framework has been evaluated with the previous six dynamic change detection algorithms or frameworks and it performs very well to detect microscopic as well as macroscopic human-understandable changes. The number of change patterns extracted in this approach was higher than the previous approaches which help to reduce the information loss