With the rapid emergence of graph representation learning, the construction
of new large-scale datasets is necessary to distinguish model capabilities and
accurately assess the strengths and weaknesses of each technique. By carefully
analyzing existing graph databases, we identify 3 critical components important
for advancing the field of graph representation learning: (1) large graphs, (2)
many graphs, and (3) class diversity. To date, no single graph database offers
all these desired properties. We introduce MalNet, the largest public graph
database ever constructed, representing a large-scale ontology of malicious
software function call graphs. MalNet contains over 1.2 million graphs,
averaging over 15k nodes and 35k edges per graph, across a hierarchy of 47
types and 696 families. Compared to the popular REDDIT-12K database, MalNet
offers 105x more graphs, 39x larger graphs on average, and 63x more classes. We
provide a detailed analysis of MalNet, discussing its properties and
provenance, along with the evaluation of state-of-the-art machine learning and
graph neural network techniques. The unprecedented scale and diversity of
MalNet offers exciting opportunities to advance the frontiers of graph
representation learning--enabling new discoveries and research into imbalanced
classification, explainability and the impact of class hardness. The database
is publicly available at www.mal-net.org.

研究了图形表示学习的新兴领域，指出了增加新数据集的必要性，并介绍了一个全新的名为 MalNet 的数据库，它是公共图形数据库中最大的，提供了对马尔维尔的恶意软件功能调用图的的大规模本体，这个数据库可以用于机器学习和图形神经网络技术的评估，并提供了关于解释能力和类别难度的新的机会。