Inferring the underlying structures of diffusion networks based on observed diffusion results is a fundamental problem in network analysis. Traditional approaches typically address this problem by inferring each diffusion network in isolation, relying on the assumption that sufficient observation data is available for each individual inference task. However, in many real-world scenarios, it is common to observe diffusion processes occur across multiple networks with similar structures, while the amount of observable data collected on each network is often limited. In this work, we study how to infer multiple similar diffusion networks jointly with limited observation data for each network. To this end, we propose a novel iterative strategy which in turn updates the inference results for all diffusion networks by exploiting the similarity between the networks, and theoretically guarantee the monotonicity and convergence of the iterative process. Extensive experiments on both synthetic and real-world networks demonstrate that our method not only achieves superior inference accuracy compared to existing techniques, but also maintains high computational efficiency.

Outdoor billboard advertising has proven effective for commercial promotions, attracting potential customers, and boosting product sales. Auction serves as a popular method for leasing billboard usage rights, enabling a seller to rent billboards to winning users for predefined periods according to their bids. An effective auction algorithm is of great significance to maximize the efficiency of the billboard ecosystem. In contrast to a rich literature on Internet advertising auctions, well-crafted algorithms tailored for outdoor billboard auctions remain rare. In this work, we investigate the problem of outdoor billboard auctions, in the practical setting where bids are received and processed on the fly. Our goal is to maximize social welfare, namely the total benefits of auction participants, including the billboard service provider and the bidding users. To this end, we first formulate the billboard social welfare maximization problem into an Integer Linear Problem (ILP), and then reformulate the ILP into a compact form with a reduced size of constraints (at the cost of involving exponentially many primal variables), based on which we derive the dual problem. Furthermore, we design a dual oracle to handle the exponentially many dual constraints, avoiding exhaustive enumeration. We present a primal-dual online algorithm with an incentive-compatible pricing mechanism. Theoretical analysis proves the individual rationality, incentive compatibility, and computational efficiency of our online algorithm. Extensive experimental results show that the online algorithm is both effective and efficient, and achieves a good competitive ratio.

Motif detection is a graph algorithm that detects certain local structures in a graph. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzingonline transaction networkthat is generated in applications such as instant messaging and e-commerce. In an online transaction network, each vertex represents a user’s account and each edge represents a money transaction between two users. In this work, we try to analyze online transaction networks with network motifs. We design motif-based vertex embedding that integrates motif counts and centrality measurements. Furthermore, we design a distributed framework to detect motifs in large-scale online transaction networks. Our framework obtains the edge directions using a bi-directional tagging method and avoids redundant detection with a reduced view of neighboring vertices. We implement the proposed framework under the parameter server architecture. In the evaluation, we analyze different kinds of online transaction networks w.r.t the distribution of motifs and evaluate the effectiveness of motif-based embedding in downstream graph analytical tasks. The experimental results also show that our proposed motif detection framework can efficiently handle large-scale graphs.

To handle graphs in which features or connections are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose Benchtemp, a general benchmark for evaluating TGNN models on various workloads. Benchtemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, Benchtemp engineers a standard pipeline that unifies the TGNN evaluation. With Benchtemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made Benchtemp publicly available at https://github.com/qianghuangwhu/benchtemp and datasets at https://zenodo.org/record/8267846.