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.

This paper presents a novel time-aware language model, T-gram , to predict the human mobility using location check-in data. While the conventional n-gram language model, which use the contextual co-occurrence to estimate the probability of a sequence of items, are often employed to predict human mobility, the time information of items is merely considered. T-gram exploits the time information associated at each location, and aims to estimate the probability of visiting satisfaction for a given sequence of locations. For a location sequence, if locations are visited at right times and the transitions between locations are proper as well, the T-gram probability gets higher. We also devise a T-gram Search algorithm to predict future locations. Experiments of human mobility prediction conducted on Gowalla check-in data significantly outperform a series of n-gram-based methods and encourage the future usage of T-gram.

With the emergence of location-based services, such as Foursquare and Gowalla, users are allowed to easily perform check-in actions anywhere and anytime. The location-based check-in not only enables personal geospatial journeys but also serves as a kind of fine-grained source for trip planning. In this work, we aim to collectively compose traveling paths by leveraging the check-in data through mining the moving behaviors of users. A novel system, TP-Comp, is developed. To compose travel paths, TP-Comp not only allows users to specify starting/end and/or must-go locations, but also provides the flexibility of the time constraint requirement (i.e., the expected duration of the trip). By considering a sequence of check-in points as a traveling path, we mine the frequent sequences with some ranking mechanism to achieve the goal. Our TP-Comp targets at travelers who are unfamiliar to the objective area/city and have time limitation in the trip.