6,660 research outputs found
Robust detection of communities with multi-semantics in large attributed networks
© 2018, Springer Nature Switzerland AG. In this paper, we are interested in how to explore and utilize the relationship between network communities and semantic topics in order to find the strong explanatory communities robustly. First, the relationship between communities and topics displays different situations. For example, from the viewpoint of semantic mapping, their relationship can be one-to-one, one-to-many or many-to-one. But from the standpoint of underlying community structures, the relationship can be consistent, partially consistent or completely inconsistent. Second, it will be helpful to not only find communities more precise but also reveal the communities’ semantics that shows the relationship between communities and topics. To better describe this relationship, we introduce the transition probability which is an important concept in Markov chain into a well-designed nonnegative matrix factorization framework. This new transition probability matrix with a suitable prior which plays the role of depicting the relationship between communities and topics can perform well in this task. To illustrate the effectiveness of the proposed new approach, we conduct some experiments on both synthetic and real networks. The results show that our new method is superior to baselines in accuracy. We finally conduct a case study analysis to validate the new method’s strong interpretability to detected communities
Hierarchical Message-Passing Graph Neural Networks
Graph Neural Networks (GNNs) have become a promising approach to machine
learning with graphs. Since existing GNN models are based on flat
message-passing mechanisms, two limitations need to be tackled. One is costly
in encoding global information on the graph topology. The other is failing to
model meso- and macro-level semantics hidden in the graph, such as the
knowledge of institutes and research areas in an academic collaboration
network. To deal with these two issues, we propose a novel Hierarchical
Message-Passing Graph Neural Networks framework. The main idea is to generate a
hierarchical structure that re-organises all nodes in a graph into multi-level
clusters, along with intra- and inter-level edge connections. The derived
hierarchy not only creates shortcuts connecting far-away nodes so that global
information can be efficiently accessed via message passing but also
incorporates meso- and macro-level semantics into the learning of node
embedding. We present the first model to implement this hierarchical
message-passing mechanism, termed Hierarchical Community-aware Graph Neural
Network (HC-GNN), based on hierarchical communities detected from the graph.
Experiments conducted on eight datasets under transductive, inductive, and
few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN
models in network analysis tasks, including node classification, link
prediction, and community detection
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach
The increasing availability of temporal network data is calling for more
research on extracting and characterizing mesoscopic structures in temporal
networks and on relating such structure to specific functions or properties of
the system. An outstanding challenge is the extension of the results achieved
for static networks to time-varying networks, where the topological structure
of the system and the temporal activity patterns of its components are
intertwined. Here we investigate the use of a latent factor decomposition
technique, non-negative tensor factorization, to extract the community-activity
structure of temporal networks. The method is intrinsically temporal and allows
to simultaneously identify communities and to track their activity over time.
We represent the time-varying adjacency matrix of a temporal network as a
three-way tensor and approximate this tensor as a sum of terms that can be
interpreted as communities of nodes with an associated activity time series. We
summarize known computational techniques for tensor decomposition and discuss
some quality metrics that can be used to tune the complexity of the factorized
representation. We subsequently apply tensor factorization to a temporal
network for which a ground truth is available for both the community structure
and the temporal activity patterns. The data we use describe the social
interactions of students in a school, the associations between students and
school classes, and the spatio-temporal trajectories of students over time. We
show that non-negative tensor factorization is capable of recovering the class
structure with high accuracy. In particular, the extracted tensor components
can be validated either as known school classes, or in terms of correlated
activity patterns, i.e., of spatial and temporal coincidences that are
determined by the known school activity schedule
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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