找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復制鏈接]
樓主: 佯攻
51#
發(fā)表于 2025-3-30 11:17:41 | 只看該作者
52#
發(fā)表于 2025-3-30 13:11:55 | 只看該作者
53#
發(fā)表于 2025-3-30 17:41:35 | 只看該作者
https://doi.org/10.1007/1-4020-4097-0ion, but also effectively reduces the memory consumption at all time. We also devise an effective workload balance mechanism that is automatically triggered by the idle machines to handle skewed workloads. The experiment results demonstrate the efficiency and scalability of our proposed algorithm.
54#
發(fā)表于 2025-3-30 23:04:44 | 只看該作者
MRSCN: A GNN-based Model for?Mining Relationship Strength Changes Between Nodes in?Dynamic Networksup model. We develop two group mining algorithms. We conduct extensive experiments on real-life dynamic networks to evaluate our models. The results demonstrate the effectiveness of the proposed MRSCN model and the drastic group mining method.
55#
發(fā)表于 2025-3-31 02:05:29 | 只看該作者
An Efficient Index-Based Method for?Skyline Path Query over?Temporal Graphs with?LabelsMP nodes search and Mout set construction. Based on this index, we propose an efficient TMP algorithm to provide the skyline path query over temporal graphs with labels. Finally, extensive experiments show the effectiveness and efficiency of our proposed algorithm.
56#
發(fā)表于 2025-3-31 06:47:51 | 只看該作者
Efficient and?Scalable Distributed Graph Structural Clustering at?Billion Scaleion, but also effectively reduces the memory consumption at all time. We also devise an effective workload balance mechanism that is automatically triggered by the idle machines to handle skewed workloads. The experiment results demonstrate the efficiency and scalability of our proposed algorithm.
57#
發(fā)表于 2025-3-31 13:15:58 | 只看該作者
58#
發(fā)表于 2025-3-31 15:35:32 | 只看該作者
SRACas: A Social Role-Aware Graph Neural Network-Based Model for?Popularity Prediction of?Informatioors and change the structure and popularity of information cascades. Existing deep learning-based methods utilize several independent sub-cascade graphs or paths to learn cascade representations, which lose vital information about social roles and dynamics between sub-cascades at different moments.
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-11-2 16:24
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
萍乡市| 庄河市| 大宁县| 武宁县| 舒兰市| 巴林右旗| 肥城市| 宁远县| 利津县| 宁陵县| 灵石县| 雷山县| 仙游县| 堆龙德庆县| 四川省| 栾城县| 江安县| 刚察县| 鲁甸县| 北票市| 日土县| 长治市| 阿坝县| 灯塔市| 滕州市| 全椒县| 宣威市| 巴楚县| 唐河县| 四川省| 随州市| 青川县| 米林县| 五指山市| 彩票| 阜平县| 灵璧县| 浠水县| 北海市| 蒙山县| 德安县|