Unsupervised/self-supervised graph neural networks (GNN) are susceptible to the inherent randomness in the input graph data, which adversely affects the model's performance in downstream tasks. In this paper, we propose USER, an unsupervised and robust version of GNN based on structural entropy, to alleviate the interference of graph perturbations and learn appropriate representations of nodes without label information. To mitigate the effects of undesirable perturbations, we analyze the property of intrinsic connectivity and define the intrinsic connectivity graph. We also identify the rank of the adjacency matrix as a crucial factor in revealing a graph that provides the same embeddings as the intrinsic connectivity graph. To capture such a graph, we introduce structural entropy in the objective function. Extensive experiments conducted on clustering and link prediction tasks under random-perturbation and meta-attack over three datasets show that USER outperforms benchmarks and is robust to heavier perturbations.

This paper is the first to use contrastive learning to improve the robustness of graph representation learning for signed bipartite graphs, which are commonly found in social networks, recommender systems, and paper review platforms. Existing contrastive learning methods for signed graphs cannot capture implicit relations between nodes of the same type in signed bipartite graphs, which have two types of nodes and edges only connect nodes of different types. We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. SBGCL augments a signed bipartite graph with a novel two-level graph augmentation method. At the top level, we maintain two perspectives of the signed bipartite graph, one presents the original interactions between nodes of different types, and the other presents the implicit relations between nodes of the same type. At the bottom level, we employ stochastic perturbation strategies to create two perturbed graphs in each perspective. Then, we construct positive and negative samples from the perturbed graphs and design a multi-perspective contrastive loss to unify the node presentations learned from the two perspectives. Results show proposed model is effective over state-of-the-art methods on real-world datasets.

Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.

Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce local spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.

Synthesizing data extracted from primary studies is an integral component of the methodologies in support of Evidence Based Software Engineering (EBSE) such as System Literature Review (SLR). Since a large and increasing number of studies in Software Engineering (SE) incorporate qualitative data, it is important to systematically review and understand different aspects of the Qualitative Research Synthesis (QRS) being used in SE. We have reviewed the use of QRS methods in 328 SLRs published between 2005 and 2015. We also inquired the authors of 274 SLRs to confirm whether or not any QRS methods were used in their respective reviews. 116 of them provided the responses, which were included in our analysis. We found eight QRS methods applied in SE research, two of which, narrative synthesis and thematic synthesis, have been predominantly adopted by SE researchers for synthesizing qualitative data. Our study determines that a significant amount of missing knowledge and incomplete understanding of the defined QRS methods in the community. Our effort also identifies an initial set factors that may influence the selection and use of appropriate QRS methods in SE.

Context: The quality of an Systematic Literature Review (SLR) is as good as the quality of the reviewed papers. Hence, it is vital to rigorously assess the papers included in an SLR. There has been no tertiary study aimed at reporting the state of the practice of quality assessment used in SLRs in Software Engineering (SE).