Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

diathermy

Discovering Persistent Subgraph Patterns over?Streaming Graphsted memory usage. . maps each subgraph into the corresponding bucket based on hash functions to compute the persistence of each pattern. Then we introduce optimizations to separate persistent and non-persistent patterns, further improving the effectiveness and throughput in space-scarce scenarios. E

outer-ear

Mining Top-, Frequent Patterns over?Streaming Graphsn provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve . with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-qual

Aboveboard

肥料

0302-9743 D consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network..978-3-031-30674-7978-3-031-30675-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

發(fā)電機(jī)

0302-9743 for Advanced Applications, DASFAA 2023, held in April 2023 in Tianjin, China..The total of 125 full papers, along with 66 short papers, are presented together in this four-volume set was carefully reviewed and selected from 652 submissions. Additionally, 15 industrial papers, 15 demo papers and 4 Ph

赦免

Conference proceedings 2023ed Applications, DASFAA 2023, held in April 2023 in Tianjin, China..The total of 125 full papers, along with 66 short papers, are presented together in this four-volume set was carefully reviewed and selected from 652 submissions. Additionally, 15 industrial papers, 15 demo papers and 4 PhD consorti

溫順

jabber

伸展

Edge Coloring on?Dynamic Graphsan efficient dynamic edge coloring algorithm that only explores the edges with color change and their 2-hop incident edges to maintain the coloring. Moreover, we propose some early pruning rules to further reduce the unnecessary computation. Experimental results on real graphs demonstrate the efficiency of our approach.

bronchodilator

TE-DyGE: Temporal Evolution-Enhanced Dynamic Graph Embedding Networkd information. Additionally, we introduce temporal-dependent weight to enhance the structure attention network. We evaluate the TE-DyGE in link prediction tasks. The experimental results demonstrate a generally higher performance of TE-DyGE compared with several state-of-the-art related baselines.