The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

Spatial Crowdsourcing (SC) is a promising service that incentives workers to finish location-based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC, for which most state-of-the-art algorithms focus on designing incentive mechanisms based on the existing SC worker pool. However, they may fail when the number of SC workers is not enough, especially for the new SC platforms. In recent years, social networks have been found to be helpful for worker recruitment by selecting seed workers to spread the task information so as to inspire more social users to participate, but how to select seed workers remains a challenge. Existing methods typically require numerous iterative searches leading to inefficiency in facing the big picture and failing to cope with dynamic environments.

Heterogeneous graphs provide a universal data structure for representing various kinds of structured data in numerous domains. The graph alignment problem aims to find the correspondences of vertices in different graphs, playing a fundamental role in many downstream tasks of heterogeneous graph mining. In recent years, many graph alignment methods have been proposed, ranging from classical optimization methods , spectral methods , to embedding learning based-methods . Due to the problem's complexity, the result found by most existing methods is either a heuristic solution or a critical point in the solution space. In this paper, we propose GABoost, a graph alignment boosting algorithm that takes as input an initial alignment between two heterogeneous graphs and outputs a boosted alignment via an iterative local-optimum-escape process. One of the distinctive features of GABoost is that it can be sequentially composed with any graph alignment methods to improve the output of upstream methods. To examine the effectiveness of GABoost, we select 7 upstream methods of graph alignment as well as 6 real-world datasets, and quantitatively investigate the degree to which GABoost boosts these methods. The results show that GABoost improves the alignment accuracy of the 7 upstream methods by 25.25% on average with acceptable time overhead.

Abstract Crowdsourcing has become increasingly popular in recent years. In order to achieve the optimal task allocation, one of the most important issues is to select more suitable crowdworkers. By leveraging its pervasiveness, social network can be employed as a novel worker recruitment platform. A robust task allocation scheme over the social network could also consider the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an Non-deterministic Polynomial-Hard (NP-Hard) problem, cost-effective and budget-balanced task allocation (CBTA) problem under the WoM mode in social groups. We propose two heuristic algorithms: CB-greedy and CB-local based on greedy strategy and local search technique, respectively. We also prove that the running time of CB-greedy is $O(m^2\log m)$, whereas CB-local utilizing disjoint-set achieves $O(mn\alpha (m, n))$, where $m$ is the number of edges indicating interactions of social groups, $n$ is the number of social groups and $\alpha $ is the inverse Ackerman function. Extensive experiments validate the efficiency and performance of our proposed algorithms.

With the development of the Internet of Things and smart city, the demand for mobile crowdsensing (MCS) is increasing. Most state-of-the-art studies in MCS assume that the participants are those who have registered with the MCS platform. In this paper, we propose to exploit social network for MCS worker recruitment instead of limiting participants to the platform. MCS platform can motivate more users to join in the task by leveraging the social influence of seed workers. Inspired by this, we first propose a social influence propagation model for MCS task. Considering the constraint of budget, our objective is to maximize the effective sensing coverage by selecting a limited number of seed workers, which is formulated as MESC problem. Based on the voting theory, a heuristic algorithm named as KT Voting is proposed to select seed workers. KT Voting algorithm allows users to vote for the most influential user to themselves and add a weight to their vote based on their sensing locations. After that, seed workers are selected based on the votes received. Extensive experiments based on two real-world data sets verify the effectiveness and efficiency of the proposed KT Voting algorithm.