Higher-Order Systems
Higher-Order Systems
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高阶系统
DOI：
10.1007/978-3-030-91374-8_4
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Eriksson A
中科院分区：
文献类型：
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作者：
Eriksson A
为了将系统的结构、动力学和功能与多体相互作用联系起来，网络科学家在超图上对随机漫步进行建模，并识别出长期限制漫步的社区。两种基于流的社区检测方法马尔可夫稳定性和地图方程基于不同的原则和搜索算法来识别这样的社区。但由此产生的社区有多相似呢？我们解释了这两种方法的机械应用到超图，并比较他们在合成和现实世界的超图使用各种超边大小有偏随机游动和时间尺度。我们发现，地图方程是更敏感的时间尺度的变化和马尔可夫稳定性是更敏感的超边尺寸偏差。
To connect structure, dynamics and function in systems with multibody interactions, network scientists model random walks on hypergraphs and identify communities that confine the walks for a long time. The two flow-based community-detection methods Markov stability and the map equation identify such communities based on different principles and search algorithms. But how similar are the resulting communities? We explain both methods’ machinery applied to hypergraphs and compare them on synthetic and real-world hypergraphs using various hyperedge-size biased random walks and time scales. We find that the map equation is more sensitive to time-scale changes and that Markov stability is more sensitive to hyperedge-size biases.