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Implementation and Optimization of graph Computing Algorithms Based on graph database 1

7th China Conference on Knowledge graph and Semantic Computing (CCKS)

作者： Wei, Jiaqi Wu, Shuang Jia, Jinkang Liu, Ziqian China Telecom Cybersecur Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9789811983009

ISBN: (纸本)9789811982996;9789811983009

data mining and knowledge inferring over knowledge graph has gained particular attention and been widely applied in industry over the past years. These works can be generalized to graph computing and graph analysis tasks, such as shortest path search, hop-constrained reachability, PageRank, triangle counting, and closeness centrality computation. However, existing graph database query language (SPARQL, Gremlin, etc.) dose not implement these algorithms. Therefore, CCKS 2022 holds a benchmark of graph computing algorithm based on graph database. We implemented all these five algorithms mentioned above based on graph database and made many optimizations to improve computation efficiency. On the final leaderboard, we took the first place, which shows extremely high practicality and efficiency.

关键词： graph computing graph data management graph data analysis

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A Hierarchical Contraction Scheme for Querying Big graphs 22

A Hierarchical Contraction Scheme for Querying Big Graphs

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International Conference on management of data (SIGMOD)

作者： Fan, Wenfei Li, Yuanhao Liu, Muyang Lu, Can Univ Edinburgh Edinburgh Midlothian Scotland Shenzhen Inst Comp Sci Shenzhen Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781450392495

This paper proposes a scheme for querying big graphs with a single machine. The scheme iteratively contracts regular structures into supernodes and builds a hierarchy of contracted graphs, until the one at the top fits into the memory. For each query class Q in use, supernodes carry synopses So such that queries of Q are answered by using SO if possible, and otherwise by drilling down to the next level with decontraction of a bounded size. Moreover, we show how to adapt a variety of existing sequential (single-machine) algorithms to the hierarchy by reusing their logic and data structures. We also provide a bounded incremental algorithm to maintain the contracted graphs in response to updates, such that its cost is determined by the sizes of changes to the input and output only. Using real-life and synthetic graphs, we experimentally verify that with a single machine, the hierarchy is able to compute exact query answers when memory is as small as 7.6% of graphs, speeds up various applications by 9.8 times on average, and is even 120.1 times faster than some parallel graph systems that use 6 machines.

关键词： graph data management graph contraction graph algorithms

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FoodBroker - Generating Synthetic datasets for graph-Based Business Analytics 5th

FoodBroker - Generating Synthetic Datasets for Graph-Based B...

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5th International Workshop on Big data Benchmarking (WBDB)

作者： Petermann, Andre Junghanns, Martin Mueller, Robert Rahm, Erhard Univ Leipzig D-04109 Leipzig Germany Leipzig Univ Appl Sci Leipzig Germany

ISBN: (纸本)9783319202334;9783319202327

We present FoodBroker, a new data generator for bench-marking graph-based business intelligence systems and approaches. It covers two realistic business processes and their involved master and transactional data objects. The interactions are correlated in controlled ways to enable non-uniform distributions for data and relationships. For benchmarking data integration, the generated data is stored in two interrelated databases. The dataset can be arbitrarily scaled and allows comprehensive graph-and pattern-based analysis.

关键词： Synthetic data generation Benchmarks graph data management Business intelligence

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Time-Dependent graphs: Definitions, Applications, and Algorithms

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data SCIENCE AND ENGINEERING 2019年第4期4卷 352-366页

作者： Wang, Yishu Yuan, Ye Ma, Yuliang Wang, Guoren Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

A time-dependent graph is, informally speaking, a graph structure dynamically changes with time. In such graphs, the weights associated with edges dynamically change over time, that is, the edges in such graphs are activated by sequences of time-dependent elements. Many real-life scenarios can be better modeled by time-dependent graphs, such as bioinformatics networks, transportation networks, and social networks. In particular, the time-dependent graph is a very broad concept, which is reflected in the related research with many names, including temporal graphs, evolving graphs, time-varying graphs, historical graphs, and so on. Though static graphs have been extensively studied, for their time-dependent generalizations, we are still far from a complete and mature theory of models and algorithms. In this paper, we discuss the definition and topological structure of time-dependent graphs, as well as models for their relationship to dynamic systems. In addition, we review some classic problems on time-dependent graphs, e.g., route planning, social analysis, and subgraph problem (including matching and mining). We also introduce existing time-dependent systems and summarize their advantages and limitations. We try to keep the descriptions consistent as much as possible and we hope the survey can help practitioners to understand existing time-dependent techniques.

关键词： Time-dependent network graph data management Network analysis graph system

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graph Indexing for Shortest-Path Finding over Dynamic Sub-graphs 16

Graph Indexing for Shortest-Path Finding over Dynamic Sub-Gr...

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ACM SIGMOD International Conference on management of data

作者： Hassan, Mohamed S. Aref, Walid G. Aly, Ahmed M. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450335317

A variety of applications spanning various domains, e.g., social networks, transportation, and bioinformatics, have graphs as first-class citizens. These applications share a vital operation, namely, finding the shortest path between two nodes. In many scenarios, users are interested in filtering the graph before finding the shortest path. For example, in social networks, one may need to compute the shortest path between two persons on a sub-graph containing only family relationships. This paper focuses on dynamic graphs with labeled edges, where the target is to find a shortest path after filtering some edges based on user-specified query labels. This problem is termed the Edge-Constrained Shortest Path query (or ECSP, for short). This paper introduces Edge-Disjoint Partitioning (IMP, for short), a new technique for efficiently answering ECSP queries over dynamic graphs. NIP has two main components: a dynamic index that is based on graph partitioning, and a traversal algorithm that exploits the regular patterns of the answers of ECSP queries. EDP partitions the graph based on the labels of the edges. On demand, EDP computes specific sub-paths within each partition and updates its index. The computed sub-paths are cached and can be leveraged by future queries. To answer an ECSP query, EDP connects sub-paths from different partitions using its efficient traversal algorithm. EDP can dynamically handle various types of graph updates, e.g., label, edge, and node updates. The index entries that are potentially affected by graph updates are invalidated and are re-computed on demand. EDP is evaluated using real graph datasets from various domains. Experimental results demonstrate that EDP can achieve query performance gains of up to four orders of magnitude in comparison to state of the art techniques.

关键词： shortest path graph indexing query processing query optimization graph query graph partitioning graph data management

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Join Cardinality Estimation graphs: Analyzing Pessimistic and Optimistic Estimators Through a Common Lens

Join Cardinality Estimation Graphs: Analyzing Pessimistic an...

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作者： Chen, Jeremy Yujui University of Waterloo

学位级别：硕士

Join cardinality estimation is a fundamental problem that is solved in the query optimizers of database management systems when generating efficient query plans. This problem arises both in systems that manage relational data as well those that manage graph-structured data where systems need to estimate the cardinalities of subgraphs in their input graphs. We focus on graph-structured data in this thesis. A popular class of join cardinality estimators uses statistics about sizes of small size queries to make estimates for larger queries. Statistics-based estimators can be broadly divided into two groups: (i) optimistic estimators that use statistics in formulas that make degree regularity and conditional independence assumptions; and (ii) the recent pessimistic estimators that estimate the sizes of queries using a set of upper bounds derived from linear programs, such as the AGM bound, or tighter bounds, such as the MOLP bound that are based on information theoretic bounds. In this thesis, we introduce a new framework that we call cardinality estimation graph (CEG) that can represent the estimates of both optimistic and pessimistic estimators. We observe that there is generally more than one way to generate optimistic estimates for a query, and the choice has either been ad-hoc or unspecified in previous work. We empirically show that choosing the largest candidate yields much higher accuracy than pessimistic estimators across different datasets and query workloads, and it is an effective heuristic to combat underestimations, which optimistic estimators are known to suffer from. To further improve the accuracy, we demonstrate how hash partitioning, an optimization technique designed to improve pessimistic estimators' accuracy, can be applied to optimistic estimators, and we evaluate the effectiveness. CEGs can also be used to obtain insights of pessimistic estimators. We show MOLP estimator is at least as tight as the pessimistic estimator and are identical on acycl

关键词： graph data management query optimization cardinality estimation

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A unified framework for effective team formation in social networks

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 177卷 114886-114886页

作者： Selvarajah, Kalyani Zadeh, Pooya Moradian Kobti, Ziad Palanichamy, Yazwand Kargar, Mehdi Univ Windsor Sch Comp Sci Windsor ON Canada Ryerson Univ Ted Rogers Sch Management Toronto ON Canada

Collaboration networks are social networks in which nodes represent experts, and edges represent the interactions between them. Team Formation Problem (TFP) in Social Networks (SN) is to construct a group of individuals to work on complex tasks. Teams should satisfy the skill set required by the tasks and can collaborate effectively under multiple constraints. Although many algorithms have been proposed to confront the TFP, most of them optimize different criteria and various parameters (e.g. communication cost or expertise level). There is no unified framework to incorporate the most significant parameters towards formulating effective teams of experts. We propose a unified framework for the TFP in SN based on a multi-objective cultural algorithm that involves the integration of essential cost functions such as communication cost, expertise level, collective trust score, and geological proximity. Since these are conflicting objectives, we return a set of Pareto front of teams that are not dominated by other feasible teams with regards to any of the objectives. Moreover, we examine the temporal nature of both communication costs and expertise levels in our model and introduce a new method to formulate them. We introduce a profile similarity formula to express the trust score. We then discuss the importance of emotional index in TFP. Our model is tested on a benchmark table, which is generated with various criteria of social networks. Our model is then compared with NSGA II, graph-Based and Exhaustive search.

关键词： Team formation Social networks graph data management Multi-objective optimization

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HT: A Novel Labeling Scheme for k-Hop Reachability Queries on DAGs

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IEEE ACCESS 2019年 7卷 172110-172122页

作者： Du, Ming Yang, Anping Zhou, Junfeng Tang, Xian Chen, Ziyang Zuo, Yanfei Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201620 Peoples R China Histo Pathol Diagnost Ctr Shanghai 200030 Peoples R China

Given a directed acyclic graph (DAG), a k-hop reachability query u ->(?k) v is used to answer whether there exists a path from u to v with length <= k. Answering k-hop reachability queries is a fundamental graph operation and has been extensively studied during the past years. Considering that existing approaches still suffer from inefficiency in practice when processing large graphs, we propose a novel labeling scheme, namely HT, to accelerate k-hop reachability queries answering. HT uses a constrained 2hop distance label to maintain the length of shortest paths between a set of hop nodes and other nodes, and for the remaining reachability information, HT uses a novel topological level to accelerate graph traversal. Further, we propose to enhance HT by two optimization techniques. The experimental results show that compared with the state-of-the-art approaches, HT works best for most graphs when answering k-hop reachability queries with small index size and reasonable index construction time.

关键词： graph data management reachability queries processing k-hop reachability

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Compact group discovery in attributed graphs and social networks

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INFORMATION PROCESSING & management 2020年第2期57卷 102054-000页

作者： Khan, Abeer Golab, Lukasz Kargar, Mehdi Szlichta, Jaroslaw Zihayat, Morteza Univ Waterloo Waterloo ON Canada Ryerson Univ Ted Rogers Sch Management Toronto ON Canada Ontario Tech Univ Oshawa ON Canada

Social networks and many other graphs are attributed, meaning that their nodes are labelled with textual information such as personal data, expertise or interests. In attributed graphs, a common data analysis task is to find subgraphs whose nodes contain a given set of keywords. In many applications, the size of the subgraph should be limited (i.e., a subgraph with thousands of nodes is not desired). In this work, we introduce the problem of compact attributed group (AG) discovery. Given a set of query keywords and a desired solution size, the task is to find subgraphs with the desired number of nodes, such that the nodes are closely connected and each node contains as many query keywords as possible. We prove that finding an optimal solution is NP-hard and we propose approximation algorithms with a guaranteed ratio of two. Since the number of qualifying AGs may be large, we also show how to find approximate top-k AGs with polynomial delay. Finally, we experimentally verify the effectiveness and efficiency of our techniques on real-world graphs.

关键词： Attributed graphs Social networks graph data management Approximation algorithms

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General-purpose query processing on summary graphs

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SOCIAL NETWORK ANALYSIS AND MINING 2024年第1期14卷 157-157页

作者： Anagnostopoulos, Aris Arrigoni, Valentina Gullo, Francesco Salvatori, Giorgia Severini, Lorenzo Sapienza Univ Rome Italy UniCredit Milan Italy Univ Aquila Laquila Italy Target Reply Rome Italy

graph summarization is a well-established problem in large-scale graph data management. Its goal is to produce a summary graph, which is a coarse-grained version of a graph, whose use in substitution for the original graph enables downstream task execution and query processing at scale. Despite the extensive literature on graph summarization, still nowadays query processing on summary graphs is accomplished by either reconstructing the original graph, or in a query-specific manner. No general methods exist that operate on the summary graph only, with no graph reconstruction. In this paper, we fill this gap, and study for the first time general-purpose (approximate) query processing on summary graphs. This is a new important tool to support data-management tasks that rely on scalable graph query processing, including social network analysis. We set the stage of this problem, by devising basic, yet principled algorithms, and thoroughly analyzing their peculiarities and capabilities of performing well in practice, both conceptually and experimentally. The ultimate goal of this work is to make researchers and practitioners aware of this so-far overlooked problem, and define an authoritative starting point to stimulate and drive further research.

关键词： graph data management Scalable graph processing graph summarization Query processing

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