Intrinsically motivated exploration has proven useful for reinforcement
learning, even without additional extrinsic rewards. When the environment is
naturally represented as a graph, how to guide exploration best remains an open
question. In this work, we propose a novel approach for exploring
graph-structured data motivated by two theories of human curiosity: the
information gap theory and the compression progress theory. The theories view
curiosity as an intrinsic motivation to optimize for topological features of
subgraphs induced by the visited nodes in the environment. We use these
proposed features as rewards for graph neural-network-based reinforcement
learning. On multiple classes of synthetically generated graphs, we find that
trained agents generalize to larger environments and to longer exploratory
walks than are seen during training. Our method computes more efficiently than
the greedy evaluation of the relevant topological properties. The proposed
intrinsic motivations bear particular relevance for recommender systems. We
demonstrate that curiosity-based recommendations are more predictive of human
behavior than PageRank centrality for several real-world graph datasets,
including MovieLens, Amazon Books, and Wikispeedia.

通过结合人类好奇心理论，使用基于图神经网络的强化学习来探索图形化数据，这种内部动机探索方法尤其适用于推荐系统

利用人类好奇心的网络理论进行本质动机的图形探索

Intrinsically motivated graph exploration using network theories of  human curiosity

The impact of continually evolving digital technologies and the proliferation
of communications and content has now been widely acknowledged to be central to
understanding our world. What is less acknowledged is that this is based on the
successful arousing of curiosity both at the collective and individual levels.
Advertisers, communication professionals and news editors are in constant
competition to capture attention of the digital population perennially shifty
and distracted. This paper, tries to understand how curiosity works in the
digital world by attempting the first ever work done on quantifying human
curiosity, basing itself on various theories drawn from humanities and social
sciences. Curious communication pushes people to spot, read and click the
message from their social feed or any other form of online presentation. Our
approach focuses on measuring the strength of the stimulus to generate reader
curiosity by using unsupervised and supervised machine learning algorithms, but
is also informed by philosophical, psychological, neural and cognitive studies
on this topic. Manually annotated news headlines - clickbaits - have been
selected for the study, which are known to have drawn huge reader response. A
binary classifier was developed based on human curiosity (unlike the work done
so far using words and other linguistic features). Our classifier shows an
accuracy of 97% . This work is part of the research in computational humanities
on digital politics quantifying the emotions of curiosity and outrage on
digital media.

本研究旨在通过使用人文和社会科学中的各种理论以及无监督和监督机器学习算法，以及人类好奇心作为二元分类器的基础，试图量化人类在数字世界中的好奇心，并通过人工注释的新闻标题（clickbaits）来解释如何测量刺激产生读者好奇心的强度，以推动数字媒体中好奇心和愤怒的情感定量研究。