"A [**hypergraph**](https://en.wikipedia.org/wiki/Hypergraph) consists of *nodes* and *hyperedges*. Contrary to edges in graphs, a *hyperedge* can connect arbitrary number of nodes. For instance, the following figure shows a hypergraph with 11 nodes and 5 hyperedges drawn in different colors.\n",
"A [hypergraph](https://en.wikipedia.org/wiki/Hypergraph) consists of *nodes* and *hyperedges*. Contrary to edges in graphs, a *hyperedge* can connect arbitrary number of nodes. For instance, the following figure shows a hypergraph with 11 nodes and 5 hyperedges drawn in different colors.\n",
"Hypergraphs are particularly useful when the relationships between data points within the dataset is not binary. For instance, more than two products can be co-purchased together in an e-commerce system, so the relationship of co-purchase is $n$-ary rather than binary, and therefore it is better described as a hypergraph rather than a normal graph.\n",
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@@ -191,6 +191,7 @@
"$$f(X^{(l)}, H; W^{(l)}) = \\sigma(L X^{(l)} W^{(l)})$$$$L = D_v^{-1/2} H B D_e^{-1} H^\\top D_v^{-1/2}$$\n",
"\n",
"where\n",
"\n",
"* $H \\in \\mathbb{R}^{N \\times M}$ is the incidence matrix of hypergraph with $N$ nodes and $M$ hyperedges.\n",
"* $D_v \\in \\mathbb{R}^{N \\times N}$ is a diagonal matrix representing node degrees, whose $i$-th diagonal element is $\\sum_{j=1}^M H_{ij}$.\n",
"* $D_e \\in \\mathbb{R}^{M \\times M}$ is a diagonal matrix representing hyperedge degrees, whose $j$-th diagonal element is $\\sum_{i=1}^N H_{ij}$.\n",
