Every graph hides a tree: through tree decomposition—a foundational tool in modern graph theory with broad applications such as in computational power networks, any network can be unfolded into a hierarchy of overlapping vertex bags whose backbone is a tree. Leveraging this powerful lens, we propose Topological Decomposition for Self-supervised Learning (TopDSL), a framework that injects multi-scale signals into graph representation learning. Concretely, we: 1) decompose the input graph into tree structures with bags representing local structural contexts; 2) compute bag-level roles via closeness centrality for nodes and local edge betweenness for edges, and aggregate these scores across bags to capture context-dependent importance (e.g., local structural bridges); 3) convert the resulting importance and attribute-stability scores into a context-aware augmentation policy that adaptively perturbs nodes, edges, and features—preserving local bridges, honoring multi-community vertices, and attenuating noisy global hubs; 4) construct a new structural similarity loss for contrastive learning, which fuses traditional graph-based proximity with a novel tree-based similarity derived from node co-occurrence in decomposition bags; 5) demonstrate that our framework achieves superior performance over state-of-the-art baselines on various graph learning benchmarks.

Multi-Graph Learning (MGL) is a fundamental machine learning paradigm that represents objects as bags-of-graphs, each encoding a distinct structural property, and has broad applications in bioinformatics, chemistry, computing power networks, and software defect detection. However, the inherent scarcity of labeled data poses a significant bottleneck for supervised MGL approaches. While self-supervised contrastive learning offers a compelling solution, its direct application to MGL faces three key challenges: (1) existing graph neural networks, primarily for single-graph modeling, struggle to yield discriminative bag-level representations from bags-of-graphs; (2) conventional contrastive objectives are limited to single-level settings, failing to capture cross-hierarchical dependencies; and (3) standard data augmentation often disrupts intrinsic graph and bag structures, undermining semantic consistency. To address these issues, we propose the Hierarchical Graph-Bag-Network (HGBN), a self-supervised MGL framework that constructs hierarchical representations in the form of a graph-bag-network. HGBN employs an asymmetric hierarchical graph neural network to learn discriminative graph-level and bag-level representations, introduces cross-hierarchical contrastive objectives to align graph-level and bag-level semantics, and leverages the asymmetric network outputs to form positive and negative pairs, preserving intrinsic structural and semantic consistency. Experiments on eight benchmark multi-graph datasets demonstrate that HGBN consistently outperforms both supervised and self-supervised state-of-the-art baselines, achieving average improvements of 4.82% in accuracy and F1 score.

Network resilience refers to the ability of a network to maintain its functionality despite perturbations, where resilience/robustness is shown when a substantial proportion of its nodes remain engaged even under changes. Such phenomenon is common in real-world networks, such as computing power networks. Previous works demonstrate that the coreness of a user/node effectively captures the dynamics of user engagement. However, most existing works only consider changes in a single coreness value and thus fail to measure the overall network resilience. Subsequent works are either inefficient or do not consider coreness-decreased scenario. In this paper, we propose and study the collapsed follower maximization problem, aiming to maximize the number of coreness-decreased vertices by finding a group collapser (collapsing a set of vertices) with a given budget. We prove that the problem is NP-hard and W[2]-hard parameterized by the budget b. To address the problem, we first present a Greedy algorithm that iteratively finds the best collapser in each of the budget b iterations. To further optimize the Greedy algorithm, we propose GreedyOpt, which leverages the shell component structure to accelerate the computation of follower for one collapser and prune the search space. Extensive experimental results on 8 real-world datasets show that the effectiveness and efficiency of our algorithms.

Graph Neural Networks (GNNs) are the rising standard for graph tasks, yet their distributed training in servers or computing power network remains challenging. Cross-machine sampling and data loading often create bottlenecks, leading to inefficient resource utilization. In this paper, we present Hybrid2, a distributed GNN training system that combines full-graph and mini-batch training through a novel hybrid-batch training method. It also adopts hybrid feature extraction, leveraging both local caching and remote access to improve feature retrieval efficiency. The integration of these methods in Hybrid² results in a dual hybrid-gain effect. First, it reduces sampling and loading overhead by pre-aggregating neighbors for each target vertex, minimizing the layers to sample and load. Second, it accelerates data loading by dynamically identifying and locally caching the most frequently accessed vertices during training, maximizing memory efficiency. Experimental results demonstrate that Hybrid² brings substantial performance improvements across key components of distributed GNN training. Network communication overhead is reduced by up to tens of times, while both sampling and loading achieve at least several-fold speedups. These gains contribute to an overall training acceleration exceeding 20× compared to DistDGL, all with comparable GPU memory usage and no loss in accuracy. Compared to the state-of-the-art system, it achieves nearly 3× speedup while using fewer resources.

To be still yet still moving. - Do Hyun Choe

Attributed Heterogeneous Information Networks (AHINs) amalgamate the advantages of attributed graphs (AGs) and heterogeneous information networks (HINs) to model intri-cate systems. Within this context, community search-aiming to identify the most probable community containing the queried ver-tex-has been extensively explored in AGs and HINs. However, existing methodologies fall short in simultaneously accommodating heterogeneous attributes and multiple meta-paths in AHINs, posing a substantial challenge in investigating community search within expansive AHINs. Recent studies highlight the efficacy of machine learning-based community search, offering enhanced flexibility and higher-quality communities in comparison to traditional structural-based methods. Yet, semi-supervised learning methods demand substantial labeled data and incur considerable memory and time costs when applied to large AHINs. To tackle these challenges, we propose a MK (Most-likely; K-sized) community search approach. This approach involves defining an MK community and leveraging Graph Neural Networks (GNNs) to amalgamate structures and attributes into a unified goodness metric. Our methodology involves training on local subgraphs sampled via guided random walks based on multiple meta-paths, circumventing the need for training on the entire graph. Moreover, attention-based GNNs adeptly learn meta-path weights to guide weighted walks in subsequent iterations. Additionally, self-training is employed to alleviate the labeling burden. We also demonstrate that pinpointing the location for the MK community is NP-hard and present a heuristic local search strategy that expedites the resolution process through rewriting. Ultimately, the convergence of iterations yields the solution. Extensive experiments conducted on four real-world datasets underscore that the MK framework significantly enhances both effectiveness and efficiency in community search within AHINs. Our code is publicly available at https://github.com/uucxuu/CSAH.

Community search is a fundamental problem in graph analysis. However, prevailing community search models predominantly focus on non-attributed or vertex-attributed graphs. Real-world graphs often bear crucial information within their edges, depicting intricate interactions among vertices. Integrating this edge-based information becomes pivotal in refining community search methodologies. In this paper, we proposed the Edge-Attributed Community Search (EACS) problem and proved that the EACS problem is NP-hard. Advanced exact and 2-approximation algorithms are proposed to address the EACS problem. Extensive experiments demonstrate the efficiency and effectiveness of our algorithms.

Community search is a fundamental graph processing task that aims to find a community containing the given query node. Recent studies show that machine learning (ML)-based community search can return higher-quality communities than the classic methods such as k-core and k-truss. However, the state-of-the-art ML-based models require a large number of labeled data (i.e., nodes in ground-truth communities) for training that are difficult to obtain in real applications, and incur unaffordable memory costs or query time for large datasets. To address these issues, in this paper, we present the community search based on contrastive learning with partition, namely COCLEP, which only requires a few labels and is both memory and query efficient. In particular, given a small collection of query nodes and a few (e.g., three) corresponding ground-truth community nodes for each query, COCLEP learns a query-dependent model through the proposed graph neural network and the designed label-aware contrastive learner. The former perceives query node information, low-order neighborhood information, and high-order hypergraph structure information, the latter contrasts low-order intra-view, high-order intra-view, and low-high-order inter-view representations of the nodes. Further, we theoretically prove that COCLEP can be scalable to large datasets with the min-cut over the graph. To the best of our knowledge, this is the first attempt to adopt contrastive learning for community search task that is nontrivial. Extensive experiments on real-world datasets show that COCLEP simultaneously achieves better community effectiveness and comparably high query efficiency while using fewer labels compared with the-state-of-the-art approaches and is scalable for large datasets.

This paper presents a novel spatial query, skyline micro-cluster (SMC) query. Given a set of data points P, a query point q, a radius γ and a density parameter k, the SMC query returns the skyline micro-clusters (MCs), where MC is a set of points in P that can be covered by a circle with radius γ and the number of points in MC is at least k. In this paper, we formally define the SMC query. As the brute-force approach to solving the SMC query in massive datasets has high computation and memory costs, we propose a basic skyline micro-cluster query algorithm, BSMC, which can reduce the time complexity from O(2N) to O(N3). Furthermore, on top of BSMC, we propose an efficient skyline micro-cluster query algorithm (ESMC). In ESMC, we use the z-value index and propose a filter to remove the invalid micro-clusters, which reduces significant computation overhead. To reduce the memory overhead, we propose an incremental skyline query method. A comprehensive performance study is conducted on real datasets and the experimental results show that our proposed method, ESMC, can significantly improve the SMC query performance.

Given a graph, searching for a community containing a query vertex is a fundamental problem and has found many applications. Most existing community search models are based on non-attributed or vertex-attributed graphs. In many real-world graphs, however, the edges carry the richest information to describe the interactions between vertices; hence, it is important to take the information into account in community search. In this paper, we conduct a pioneer study on the community search on edge-attributed graphs. We proposed the Edge-Attributed Community Search (EACS) problem, which aims to extract a subgraph that contains the given query vertex while its edges have the maximum attribute similarity. We prove that the EACS problem is NP-hard and propose both exact and 2-approximation algorithms to address EACS. Our exact algorithms run up to 2320.34 times faster than the baseline solution. Our approximate algorithms further improve the efficiency by up to 2.93 times. We conducted extensive experiments to demonstrate the efficiency and effectiveness of our algorithms.

Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density ($\rho$) and distance ($\delta$) of every point. As such, though DP has been very effective in producing high quality clusters, their complexity is O($N^2$) where$N$is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. In FDDP, we first employ the z-value index to map multi-dimensional data points into one dimensional space, and then range-partition the data according to the z-value to balance the load across the processing nodes. We ensure minimal overlapping range to handle computations at the boundary points. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate$\rho$linearly. Additionally, we propose another algorithm, CB, which uses a caching and efficient searching strategy to compute$\delta$. Moreover, FDDP is able to reduce the time complexity from$O(N^2)$to$O(N\cdot log(N))$. We provide a theoretical analysis of FDDP and evaluated FDDP empirically. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly.

Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density (ρ) and distance (δ) of every point, and the time complexity is O(N2) where N is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate ρ linearly, and CB, which uses a caching and efficient searching strategy to compute δ. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly.

Graphs are a powerful and versatile data structure that easily captures real life relationship. Multi-graph Multi-label learning (MGML) is a supervised learning task, which aims to learn a Multi-label classifier to label a set of objects of interest (e.g. image or text) with a bag-of-graphs representation. However, prior techniques on the MGML are developed based on transferring graphs into instances that does not fully utilize the structure information in the learning, and focus on learning the unseen labels only at the bag level. There is no existing work studying how to label the graphs within a bag that is of importance in many applications like image or text annotation. To bridge this gap, in this paper, we present a novel coarse and fine-grained Multi-graph Multi-label (cfMGML) learning framework which directly builds the learning model over the graphs and empowers the label prediction at both the coarse (aka. bag) level and fine-grained (aka. graph in each bag) level. In particular, given a set of labeled multi-graph bags, we design the scoring functions at both graph and bag levels to model the relevance between the label and data using specific graph kernels. Meanwhile, we propose a thresholding rank-loss objective function to rank the labels for the graphs and bags and minimize the hamming-loss simultaneously at one-step, which aims to address the error accumulation issue in traditional rank-loss algorithms. To tackle the non-convex optimization problem, we further develop an effective sub-gradient descent algorithm to handle high-dimensional space computation required in cfMGML. Experiments over various real-world datasets demonstrate cfMGML achieves superior performance than the state-of-arts algorithms.

Shared-nothing architecture has been widely used in distributed databases to achieve good scalability. While it offers superior performance for local transactions, the overhead of processing distributed transactions can degrade the system performance significantly. The key contributor to the degradation is the expensive two-phase commit (2PC) protocol used to ensure atomic commitment of distributed transactions. In this paper, we propose a transaction management scheme called LEAP to avoid the 2PC protocol within distributed transaction processing. Instead of processing a distributed transaction across multiple nodes, LEAP converts the distributed transaction into a local transaction. This benefits the processing locality and facilitates adaptive data repartitioning when there is a change in data access pattern. Based on LEAP, we develop an online transaction processing (OLTP) system, L-Store, and compare it with the state-of-the-art distributed in-memory OLTP system, H-Store, which relies on the 2PC protocol for distributed transaction processing, and H^L-Store, a H-Store that has been modified to make use of LEAP. Results of an extensive experimental evaluation show that our LEAP-based engines are superior over H-Store by a wide margin, especially for workloads that exhibit locality-based data accesses.

Efficient and scalable stream joins play an important role in performing real-time analytics for many cloud applications. However, like in conventional database processing, online theta-joins over data streams are computationally expensive and moreover, being memory-based processing, they impose high memory requirement on the system. In this paper, we propose a novel stream join model, called join-biclique, which organizes a large cluster as a complete bipartite graph. Join-biclique has several strengths over state-of-the-art techniques, including memory-efficiency, elasticity and scalability. These features are essential for building efficient and scalable streaming systems. Based on join-biclique, we develop a scalable distributed stream join system, BiStream, over a large-scale commodity cluster. Specifically, BiStream is designed to support efficient full-history joins, window-based joins and online data aggregation. BiStream also supports adaptive resource management to dynamically scale out and down the system according to its application workloads. We provide both theoretical cost analysis and extensive experimental evaluations to evaluate the efficiency, elasticity and scalability of BiStream.

Attributed graphs are becoming important tools for modeling information networks, such as the Web and various social networks (e.g. Facebook, LinkedIn, Twitter). However, it is computationally challenging to manage and analyze attributed graphs to support effective decision making. In this paper, we propose, Pagrol, a parallel graph OLAP (Online Analytical Processing) system over attributed graphs. In particular, Pagrol introduces a new conceptual Hyper Graph Cube model (which is an attributed-graph analogue of the data cube model for relational DBMS) to aggregate attributed graphs at different granularities and levels. The proposed model supports different queries as well as a new set of graph OLAP Roll-Up/Drill-Down operations. Furthermore, on the basis of Hyper Graph Cube, Pagrol provides an efficient MapReduce-based parallel graph cubing algorithm, MRGraph-Cubing, to compute the graph cube for an attributed graph. Pagrol employs numerous optimization techniques: (a) a self-contained join strategy to minimize I/O cost; (b) a scheme that groups cuboids into batches so as to minimize redundant computations; (c) a cost-based scheme to allocate the batches into bags (each with a small number of batches); and (d) an efficient scheme to process a bag using a single MapReduce job. Results of extensive experimental studies using both real Facebook and synthetic datasets on a 128-node cluster show that Pagrol is effective, efficient and scalable.

In many scientific applications, it is critical to determine if there is a relationship between a combination of objects. The strength of such an association is typically computed using some statistical measures. In order not to miss any important associations, it is not uncommon to exhaustively enumerate all possible combinations of a certain size. However, discovering significant associations among hundreds of thousands or even millions of objects is a computationally intensive job that typically takes days, if not weeks, to complete. We are, therefore, motivated to provide efficient and practical techniques to speed up the processing exploiting parallelism. In this paper, we propose a framework, COSAC, for such combinatorial statistical analysis for large-scale data sets over a MapReduce-based cloud computing platform. COSAC operates in two key phases: 1) In the distribution phase, a novel load balancing scheme distributes the combination enumeration tasks across the processing units; 2) In the statistical analysis phase, each unit optimizes the processing of the allocated combinations by salvaging computations that can be reused. COSAC also supports a more practical scenario, where only a selected subset of objects need to be analyzed against all the objects. As a representative application, we developed COSAC to find combinations of Single Nucleotide Polymorphisms (SNPs) that may interact to cause diseases. We have evaluated our framework on a cluster of more than 40 nodes. The experimental results show that our framework is computationally practical, efficient, scalable, and flexible.