25,906 research outputs found
Higher-order Spectral Clustering for Heterogeneous Graphs
Higher-order connectivity patterns such as small induced sub-graphs called
graphlets (network motifs) are vital to understand the important components
(modules/functional units) governing the configuration and behavior of complex
networks. Existing work in higher-order clustering has focused on simple
homogeneous graphs with a single node/edge type. However, heterogeneous graphs
consisting of nodes and edges of different types are seemingly ubiquitous in
the real-world. In this work, we introduce the notion of typed-graphlet that
explicitly captures the rich (typed) connectivity patterns in heterogeneous
networks. Using typed-graphlets as a basis, we develop a general principled
framework for higher-order clustering in heterogeneous networks. The framework
provides mathematical guarantees on the optimality of the higher-order
clustering obtained. The experiments demonstrate the effectiveness of the
framework quantitatively for three important applications including (i)
clustering, (ii) link prediction, and (iii) graph compression. In particular,
the approach achieves a mean improvement of 43x over all methods and graphs for
clustering while achieving a 18.7% and 20.8% improvement for link prediction
and graph compression, respectively
Community Detection for Multilayer Heterogeneous Network
Many real world networks consist of multiple types of nodes with edges that
are heterogeneous in nature. However, most of the existing work for community
detection only focused on homogeneous network consisting of a single layer. In
this paper, we propose a modified Degree-Corrected Stochastic Model (DCBM) for
modeling multilayer heterogeneous network. We develop a spectral clustering
method that can unify the information contained in each sub-network, and
demonstrate its efficiency to detect communities on simulated data and on
Authorship/Citation network data. As a by-product, we present a novel algorithm
called BiScore for clustering bipartite network under DCBM, and show that under
mild conditions BiScore is guaranteed to yield consistent results.Comment: We were not aware of the fact that similar ideas have already
appeared in the literature. We decide to withdraw the manuscript and
apologize for any confusio
Community detection over a heterogeneous population of non-aligned networks
Clustering and community detection with multiple graphs have typically
focused on aligned graphs, where there is a mapping between nodes across the
graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are
numerous application areas with multiple graphs that are only partially
aligned, or even unaligned. These graphs are often drawn from the same
population, with communities of potentially different sizes that exhibit
similar structure. In this paper, we develop a joint stochastic blockmodel
(Joint SBM) to estimate shared communities across sets of heterogeneous
non-aligned graphs. We derive an efficient spectral clustering approach to
learn the parameters of the joint SBM. We evaluate the model on both synthetic
and real-world datasets and show that the joint model is able to exploit
cross-graph information to better estimate the communities compared to learning
separate SBMs on each individual graph
Unsupervised Meta-path Reduction on Heterogeneous Information Networks
Heterogeneous Information Network (HIN) has attracted much attention due to
its wide applicability in a variety of data mining tasks, especially for tasks
with multi-typed objects. A potentially large number of meta-paths can be
extracted from the heterogeneous networks, providing abundant semantic
knowledge. Though a variety of meta-paths can be defined, too many meta-paths
are redundant. Reduction on the number of meta-paths can enhance the
effectiveness since some redundant meta-paths provide interferential linkage to
the task. Moreover, the reduced meta-paths can reflect the characteristic of
the heterogeneous network. Previous endeavors try to reduce the number of
meta-paths under the guidance of supervision information. Nevertheless,
supervised information is expensive and may not always be available. In this
paper, we propose a novel algorithm, SPMR (Semantic Preserving Meta-path
Reduction), to reduce a set of pre-defined meta-paths in an unsupervised
setting. The proposed method is able to evaluate a set of meta-paths to
maximally preserve the semantics of original meta-paths after reduction.
Experimental results show that SPMR can select a succinct subset of meta-paths
which can achieve comparable or even better performance with fewer meta-paths
Heterogeneous Graph Attention Network
Graph neural network, as a powerful graph representation technique based on
deep learning, has shown superior performance and attracted considerable
research interest. However, it has not been fully considered in graph neural
network for heterogeneous graph which contains different types of nodes and
links. The heterogeneity and rich semantic information bring great challenges
for designing a graph neural network for heterogeneous graph. Recently, one of
the most exciting advancements in deep learning is the attention mechanism,
whose great potential has been well demonstrated in various areas. In this
paper, we first propose a novel heterogeneous graph neural network based on the
hierarchical attention, including node-level and semantic-level attentions.
Specifically, the node-level attention aims to learn the importance between a
node and its metapath based neighbors, while the semantic-level attention is
able to learn the importance of different meta-paths. With the learned
importance from both node-level and semantic-level attention, the importance of
node and meta-path can be fully considered. Then the proposed model can
generate node embedding by aggregating features from meta-path based neighbors
in a hierarchical manner. Extensive experimental results on three real-world
heterogeneous graphs not only show the superior performance of our proposed
model over the state-of-the-arts, but also demonstrate its potentially good
interpretability for graph analysis.Comment: 10 page
Multi-View Community Detection in Facebook Public Pages
Community detection in social networks is widely studied because of its
importance in uncovering how people connect and interact. However, little
attention has been given to community structure in Facebook public pages. In
this study, we investigate the community detection problem in Facebook
newsgroup pages. In particular, to deal with the diversity of user activities,
we apply multi-view clustering to integrate different views, for example, likes
on posts and likes on comments. In this study, we explore the community
structure in not only a given single page but across multiple pages. The
results show that our method can effectively reduce isolates and improve the
quality of community structure
Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate
subsequent graph analytics tasks like link prediction and graph clustering.
Most approaches on graph embedding focus on preserving the graph structure or
minimizing the reconstruction errors for graph data. They have mostly
overlooked the embedding distribution of the latent codes, which unfortunately
may lead to inferior representation in many cases. In this paper, we present a
novel adversarially regularized framework for graph embedding. By employing the
graph convolutional network as an encoder, our framework embeds the topological
information and node content into a vector representation, from which a graph
decoder is further built to reconstruct the input graph. The adversarial
training principle is applied to enforce our latent codes to match a prior
Gaussian or Uniform distribution. Based on this framework, we derive two
variants of adversarial models, the adversarially regularized graph autoencoder
(ARGA) and its variational version, adversarially regularized variational graph
autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit
other potential variations of ARGA and ARVGA to get a deeper understanding on
our designs. Experimental results compared among twelve algorithms for link
prediction and twenty algorithms for graph clustering validate our solutions.Comment: To appear in IEEE Transactions on Cybernetics. arXiv admin note:
substantial text overlap with arXiv:1802.0440
Deep Representation Learning for Social Network Analysis
Social network analysis is an important problem in data mining. A fundamental
step for analyzing social networks is to encode network data into
low-dimensional representations, i.e., network embeddings, so that the network
topology structure and other attribute information can be effectively
preserved. Network representation leaning facilitates further applications such
as classification, link prediction, anomaly detection and clustering. In
addition, techniques based on deep neural networks have attracted great
interests over the past a few years. In this survey, we conduct a comprehensive
review of current literature in network representation learning utilizing
neural network models. First, we introduce the basic models for learning node
representations in homogeneous networks. Meanwhile, we will also introduce some
extensions of the base models in tackling more complex scenarios, such as
analyzing attributed networks, heterogeneous networks and dynamic networks.
Then, we introduce the techniques for embedding subgraphs. After that, we
present the applications of network representation learning. At the end, we
discuss some promising research directions for future work
Cooperative Interference Mitigation and Handover Management for Heterogeneous Cloud Small Cell Networks
Heterogeneous small cell network has attracted much attention to satisfy
users' explosive data traffic requirements. Heterogeneous cloud small cell
network (HCSNet), which combines cloud computing and heterogeneous small cell
network, will likely play an important role in 5G mobile communication
networks. However, with massive deployment of small cells, co-channel
interference and handover management are two important problems in HCSNet,
especially for cell edge users. In this article, we examine the problems of
cooperative interference mitigation and handover management in HCSNet. A
network architecture is described to combine cloud radio access network with
small cells. An effective coordinated multi-point (CoMP) clustering scheme
using affinity propagation is adopted to mitigate cell edge users'
interference. A low complexity handover management scheme is presented, and its
signaling procedure is analyzed in HCSNet. Numerical results show that the
proposed network architecture, CoMP clustering scheme and handover management
scheme can significantly increase the capacity of HCSNet while maintaining
users' quality of service.Comment: to appear in IEEE Wireless Communication
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
Recommender systems are becoming more and more important in our daily lives.
However, traditional recommendation methods are challenged by data sparsity and
efficiency, as the numbers of users, items, and interactions between the two in
many real-world applications increase fast. In this work, we propose a novel
clustering recommender system based on node2vec technology and rich information
network, namely N2VSCDNNR, to solve these challenges. In particular, we use a
bipartite network to construct the user-item network, and represent the
interactions among users (or items) by the corresponding one-mode projection
network. In order to alleviate the data sparsity problem, we enrich the network
structure according to user and item categories, and construct the one-mode
projection category network. Then, considering the data sparsity problem in the
network, we employ node2vec to capture the complex latent relationships among
users (or items) from the corresponding one-mode projection category network.
Moreover, considering the dependency on parameter settings and information loss
problem in clustering methods, we use a novel spectral clustering method, which
is based on dynamic nearest-neighbors (DNN) and a novel automatically
determining cluster number (ADCN) method that determines the cluster centers
based on the normal distribution method, to cluster the users and items
separately. After clustering, we propose the two-phase personalized
recommendation to realize the personalized recommendation of items for each
user. A series of experiments validate the outstanding performance of our
N2VSCDNNR over several advanced embedding and side information based
recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time
complexity than the baseline methods in online recommendations, indicating its
potential to be widely applied in large-scale systems
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