Triangle enumeration is a foundation brick for solving harder graph problems related to
social networks, the Internet and transportation, to name a few applications. This problem is …

When evaluating an iterative graph query over a large graph, systems incur significant
overheads due to repeated graph transfer across the memory hierarchy coupled with …

This study proposes a novel Approximately Balanced Tree Partitioning Algorithm (TPA) to
overcome the significant challenges in genealogical data management, encompassing the …

In recent years, Temporal Graph Neural Networks (TGNNs) have achieved great success in
learning tasks for graphs that change over time. These dynamic/temporal graphs represent …

Graph processing frameworks suffer performance degradation from under-utilization of
available memory bandwidth, because graph traversal often exhibits poor locality. A prior …

Large-scale sparse linear equation solver plays an important role in both numerical
simulation and artificial intelligence, and sparse triangular equation solver is a key step in …

Graph databases have received increased interests as many applications are handled as
graph problems. Shortest distance queries are one of the fundamental operations and have …

Cryptography has recently become a critical area to research and advance in order to
transmit information safely and securely among various entities, especially when the …

Geo-distributed (GD) training is a machine-learning technique that uses geographically
distributed data for model training. Like Federated Learning, geo-distributed machine …

Abstract “Teori Ekonomi Berbasis Big Data” merupakan pendekatan yang menggabungkan
prinsip-prinsip ekonomi tradisional dengan analisis data yang massif dan kompleks. Dalam …