Graph propagation algorithms such as PageRank and its variants are widely used in modern applications such as ranking webpages, spam detection, and recommender systems. To cope with the web-scale graphs in these applications, many distributed vertex-centric systems, a.k.a. Pregel-like systems, have been proposed that can be used to parallelize the iterative graph propagation algorithms. In this work, we propose a message compression framework for Pregel-like systems, called CompreGel, which accelerates graph propagation algorithms by significantly reducing the communication cost of message passing. In a Pregel-like system, messages are streams of key-value pairs, where keys (resp. values) are the IDs of the target vertices (resp. vertex values such as PageRank values for dispersion). Our framework compresses the keys and values separately to maximize the compression power. Specifically, CompreGel employs the lossless bitmap compression for keys by utilizing an ID renumbering technique, and it employs the popular error-bounded lossy compressor SZ for values. Moreover, we theoretically analyze how to set the error bound parameter of SZ properly to bound the accumulative error of vertex values across iterations of graph propagation. Extensive experiments show that CompreGel can improve job efficiency by up to 10 × with a 200 × message compression ratio, while achieving comparable accuracy to a Pregel-like system without message compression.

Given a graph pair (G 1 , G 2 ), graph edit distance (GED) is defined as the minimum number of edit operations converting G 1 to G 2 . GED is a fundamental operation widely used in many applications, but its exact computation is NP-hard, so the approximation of GED has gained a lot of attention. Data-driven learning-based methods have been found to provide superior results compared to classical approximate algorithms, but they directly fit the coupling relationship between a pair of vertices from their vertex features. We argue that while pairwise vertex features can capture the coupling cost (discrepancy) of a pair of vertices, the vertex coupling matrix should be derived from the vertex-pair cost matrix through a more well-established method that is aware of the global context of the graph pair, such as optimal transport. In this paper, we propose an ensemble approach that integrates a supervised learning-based method and an unsupervised method, both based on optimal transport. Our learning method, GEDIOT, is based on inverse optimal transport that leverages a learnable Sinkhorn algorithm to generate the coupling matrix. Our unsupervised method, GEDGW, models GED computation as a linear combination of optimal transport and its variant, Gromov-Wasserstein discrepancy, for node and edge operations, respectively, which can be solved efficiently without needing the ground truth. Our ensemble method, GEDHOT, combines GEDIOT and GEDGW to further boost the performance. Extensive experiments demonstrate that our methods significantly outperform the existing methods in terms of the performance of GED computation, edit path generation, and model generalizability.

SimRank is an important metric to measure the topological similarity between two nodes in a graph. In particular, single-source and top-k SimRank has numerous applications in recommendation systems, network analysis, and web mining, etc. Mathematically, given a vertex, the computation of single-machine and single-source SimRank mainly lies in matrix-matrix operations. However, it is almost impossible to directly compute on large graphs. Thus, existing works yield to two main operations: a series of random walks, and sparse matrix and dense vector multiplication operations. This brings about high computation cost for SimRank on large graphs. In real-world applications, there is always the query time and accuracy trade-off, which hinders the computation of high-precision SimRank on large-scale graphs. To handle this problem, this paper proposesClipSim, the first GPU-friendly parallel framework that accelerates the single-source SimRank on GPU with accuracy guarantee. We design a novel data structure and GPU-friendly parallel algorithms for efficient computation of all the operations of SimRank on GPU. Moreover, our theoretical derivation enables ClipSim to largely reduce the number of random walks required for each node, while maintaining the same theoretical accuracy as the state-of-the-art algorithm, ExactSim. We conduct extensive experiments on real-world and synthetic datasets to demonstrate the accuracy and efficiency of ClipSim. The results show that compared with ExactSim, ClipSim obtains single-source SimRank vectors with the same accuracy and up to 160× faster computation time.

Information freshness, which is characterized by a new performance metric called age of information (AoI), significantly influences decision making in numerous applications. In wireless sensor networks, unmanned aerial vehicle (UAV) has been widely adopted for fresh data collection. The key to applying UAV lies in UAV trajectory planning. Considering several fixed waypoints in UAV trajectory, the trajectory planning is an NP-hard combinatorial optimization problem, and is difficult to solve in practice. To well balance between the accuracy and efficiency, we propose an end-to-end AI-based framework in this paper to deal with the UAV trajectory planning within two stages. First, the hover positions of UAV and data transmission time are decided using a clustering module. Then, the AoI-minimal flight path is obtained through a neural trajectory solver. Compared with classic heuristic algorithms, the proposed AI-based framework achieves a smaller AoI with two orders of magnitude lower computational time. Besides, the proposed AI-based framework can be easily generalized to larger-scale scenarios (e.g., up to 2,000 sensor nodes) which cannot be solved by exact algorithms (e.g., dynamic programming) in a limited time. Moreover, the AI-based framework is comparable in accuracy with the commercial open-source solver Google OR-tools, but the efficiency is increased by 200%.