39,670 research outputs found
Structure of Heterogeneous Networks
Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure
Connecting Dream Networks Across Cultures
Many species dream, yet there remain many open research questions in the
study of dreams. The symbolism of dreams and their interpretation is present in
cultures throughout history. Analysis of online data sources for dream
interpretation using network science leads to understanding symbolism in dreams
and their associated meaning. In this study, we introduce dream interpretation
networks for English, Chinese and Arabic that represent different cultures from
various parts of the world. We analyze communities in these networks, finding
that symbols within a community are semantically related. The central nodes in
communities give insight about cultures and symbols in dreams. The community
structure of different networks highlights cultural similarities and
differences. Interconnections between different networks are also identified by
translating symbols from different languages into English. Structural
correlations across networks point out relationships between cultures.
Similarities between network communities are also investigated by analysis of
sentiment in symbol interpretations. We find that interpretations within a
community tend to have similar sentiment. Furthermore, we cluster communities
based on their sentiment, yielding three main categories of positive, negative,
and neutral dream symbols.Comment: 6 pages, 3 figure
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
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