In recent years, citizen science has become a larger and larger part of the
scientific community. Its ability to crowd source data and expertise from
thousands of citizen scientists makes it invaluable. Despite the field's
growing popularity, the interactions and structure of citizen science projects
are still poorly understood and under analyzed. We use the iNaturalist citizen
science platform as a case study to analyze the structure of citizen science
projects. We frame the data from iNaturalist as a bipartite network and use
visualizations as well as established network science techniques to gain
insights into the structure and interactions between users in citizen science
projects. Finally, we propose a novel unique benchmark for network science
research by using the iNaturalist data to create a network which has an unusual
structure relative to other common benchmark networks. We demonstrate using a
link prediction task that this network can be used to gain novel insights into
a variety of network science methods.

最近几年，公民科学已成为科学界日益重要的一部分。我们使用 iNaturalist 公民科学平台作为案例研究，分析公民科学项目的结构，并提出了一种新颖的网络科学研究基准方法，使用 iNaturalist 数据创建了一种与其他常见基准网络结构不同的网络。通过进行链接预测任务，我们证明这种网络可以为各种网络科学方法提供新的见解。

iNaturalist 公民科学社区的网络分析

Network Analysis of the iNaturalist Citizen Science Community

A bipartite network is a graph structure where nodes are from two distinct
domains and only inter-domain interactions exist as edges. A large number of
network embedding methods exist to learn vectorial node representations from
general graphs with both homogeneous and heterogeneous node and edge types,
including some that can specifically model the distinct properties of bipartite
networks. However, these methods are inadequate to model multiplex bipartite
networks (e.g., in e-commerce), that have multiple types of interactions (e.g.,
click, inquiry, and buy) and node attributes. Most real-world multiplex
bipartite networks are also sparse and have imbalanced node distributions that
are challenging to model. In this paper, we develop an unsupervised Dual
HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the
multiplex bipartite network into two sets of homogeneous hypergraphs and uses
spectral hypergraph convolutional operators, along with intra- and
inter-message passing strategies to promote information exchange within and
across domains, to learn effective node embedding. We benchmark DualHGCN using
four real-world datasets on link prediction and node classification tasks. Our
extensive experiments demonstrate that DualHGCN significantly outperforms
state-of-the-art methods, and is robust to varying sparsity levels and
imbalanced node distributions.

本文提出了一种无监督的 Dual HyperGraph Convolutional Network (DualHGCN) 模型，通过将多重二分网络转化为两组同质超图，并使用谱超图卷积算子以及内部和跨域间的信息传递策略来学习有效的节点嵌入，对四个真实数据集进行基准测试，展示了 DualHGCN 显著优于现有最先进方法，并且对不同的稀疏水平和不平衡节点分布具有鲁棒性。