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Malware classification and composition analysis: A survey of recent developments
A Abusitta, MQ Li, BCM Fung - Journal of Information Security and …, 2021 - Elsevier
Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …
complexity of malware design and the recent advancement of communication and …
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Graph kernels based on the $1 $-dimensional Weisfeiler-Leman algorithm and
corresponding neural architectures recently emerged as powerful tools for (supervised) …
corresponding neural architectures recently emerged as powerful tools for (supervised) …
Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs
N Karalias, A Loukas - Advances in Neural Information …, 2020 - proceedings.neurips.cc
Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …
especially in the absence of labeled instances. This work proposes an unsupervised …
Grami: Frequent subgraph and pattern mining in a single large graph
M Elseidy, E Abdelhamid, S Skiadopoulos, P Kalnis - 2015 - repository.kaust.edu.sa
Mining frequent subgraphs is an important operation on graphs; it is defined as finding all
subgraphs that appear frequently in a database according to a given frequency threshold …
subgraphs that appear frequently in a database according to a given frequency threshold …
Active learning: A survey
CC Aggarwal, X Kong, Q Gu, J Han, SY Philip - Data classification, 2014 - taylorfrancis.com
In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …
important observation is that all records are not equally important from the perspective of …
Dual-discriminative graph neural network for imbalanced graph-level anomaly detection
Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …
A survey of frequent subgraph mining algorithms
Graph mining is an important research area within the domain of data mining. The field of
study concentrates on the identification of frequent subgraphs within graph data sets. The …
study concentrates on the identification of frequent subgraphs within graph data sets. The …
Wl meet vc
C Morris, F Geerts, J Tönshoff… - … conference on machine …, 2023 - proceedings.mlr.press
Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …
State of the art and potentialities of graph-level learning
Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …
Analysis of federated and global scheduling for parallel real-time tasks
This paper considers the scheduling of parallel real-time tasks with implicit deadlines. Each
parallel task is characterized as a general directed acyclic graph (DAG). We analyze three …
parallel task is characterized as a general directed acyclic graph (DAG). We analyze three …