SHEEP, a Signed Hamiltonian Eigenvector Embedding for Proximity
SHEEP, a Signed Hamiltonian Eigenvector Embedding for Proximity
复制标题
SHEEP，用于邻近度的带符号哈密顿特征向量嵌入
DOI：
10.1038/s42005-023-01504-6
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发表时间：
2024
影响因子：
5.5
通讯作者：
Babul S
中科院分区：
文献类型：
--
作者：
Babul S
符号网络嵌入方法允许节点的低维表示，并且主要集中于将图划分为簇，从而丢失关于连续节点属性的信息。在这里，我们介绍了一种谱嵌入算法，用于理解符号图中节点之间的邻近关系，其中边可以具有正或负的权重。受物理模型的启发，我们将嵌入构造为依赖于节点间距离的哈密顿量的最小能量构型，并确定最佳嵌入维度。我们通过在合成网络和经验网络上的一系列实验表明，我们的方法(SHEEP)可以恢复连续的节点属性，其主要优点是：可重新配置为计算高效的特征向量问题，恢复可用作强平衡存在的统计检验的基态能量，以及节点极端主义的度量，计算为最优嵌入中到原点的距离。
Signed network embedding methods allow for a low-dimensional representation of nodes and primarily focus on partitioning the graph into clusters, hence losing information on continuous node attributes. Here, we introduce a spectral embedding algorithm for understanding proximal relationships between nodes in signed graphs, where edges can take either positive or negative weights. Inspired by a physical model, we construct our embedding as the minimum energy configuration of a Hamiltonian dependent on the distance between nodes and locate the optimal embedding dimension. We show through a series of experiments on synthetic and empirical networks, that our method (SHEEP) can recover continuous node attributes showcasing its main advantages: re-configurability into a computationally efficient eigenvector problem, retrieval of ground state energy which can be used as a statistical test for the presence of strong balance, and measure of node extremism, computed as the distance to the origin in the optimal embedding.
影响因子：
13.6
作者：
De Bacco C;Larremore DB;Moore C
通讯作者：
Moore C
影响因子：
3.7
作者：
Domagalski R;Neal ZP;Sagan B
通讯作者：
Sagan B
影响因子：
2.4
作者：
Kirkley, Alec;Cantwell, George T.;Newman, M. E. J.
通讯作者：
Newman, M. E. J.