Temporal interactions form the crux of numerous real-world scenarios, thus necessitating effective modeling in temporal graph representation learning. Despite extensive research within this domain, we identify a significant oversight in current methodologies: the temporal-spatial dynamics in graphs, encompassing both structural and temporal coherence, remain largely unaddressed. In an effort to bridge this research gap, we present a novel framework termed Graph Representation learning enhanced by Periodic and Community Interactions (GRPCI). GRPCI consists of two primary mechanisms devised explicitly to tackle the aforementioned challenge. Firstly, to utilize latent temporal dynamics, we propose a novel periodicity-based neighborhood aggregation mechanism that underscores neighbors engaged in a periodic interaction pattern. This mechanism seamlessly integrates the element of periodicity into the model. Secondly, to exploit structural dynamics, we design a novel contrastive-based local community representation learning mechanism. This mechanism features a heuristic dynamic contrastive pair sampling strategy aimed at enhancing the modeling of the latent distribution of local com munities within the graphs. Through the incorporation of these two mechanisms, GRPCI markedly augments the performance of graph networks. Empirical evaluations, conducted via a temporal link prediction task across five real-life datasets, attest to the superior performance of GRPCI in comparison to existing state of-the-art methodologies. The results of this study validate the efficacy of GRPCI, thereby establishing a new benchmark for future research in the field of temporal graph representation learning. Our findings underscore the importance of considering both temporal and structural consistency in temporal graph learning, and advocate for further exploration of this paradigm.

Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.

Lazy allocation strategy allows the memory management system to defer the space allocation action of objects until they are being accessed. This paper investigates the potential benefits of a lazy allocator for Java applications. A heap tracing tool is implemented by instrumenting an existing Java virtual machine HotSpot, which records useful object manipulating events at runtime. By profiling and analyzing a large number of benchmarks, we show the potential dynamic memory management optimization opportunity in Java programs. We also designed a simulation system to demonstrate the actual effects of a lazy allocator.