[Doc] fix math and link display issue (#5212)

notebooks/sparse/gcn.ipynb

@@ -71,7 +71,7 @@
         "## Graph Convolutional Layer\n",
         "\n",
         "Mathematically, the graph convolutional layer is defined as:\n",
-        "$$f(X^{(l)}, A) = \\sigma(\\hat{D}^{-\\frac{1}{2}}\\hat{A}\\hat{D}^{-\\frac{1}{2}}X^{(l)}W^{(l)})$$\n",
+        "$$f(X^{(l)}, A) = \\sigma(\\hat{D}^{-\\frac{1}{2}}\\hat{A}\\hat{D}^{-\\frac{1}{2}}X^{(l)}W^{(l)})$$",
         "with $\\hat{A} = A + I$, where $A$ denotes the adjacency matrix and $I$ denotes the identity matrix, $\\hat{D}$ refers to the diagonal node degree matrix of $\\hat{A}$ and $W^{(l)}$ denotes a trainable weight matrix. $\\sigma$ refers to a non-linear activation (e.g. relu).\n",
         "\n",
         "The code below shows how to implement it using the `dgl.sparse` package. The core operations are:\n",

notebooks/sparse/graph_diffusion.ipynb

@@ -134,9 +134,7 @@
       "cell_type": "markdown",
       "source": [
         "We use the graph convolution matrix from Graph Convolution Networks as the diffusion matrix in this example. The graph convolution matrix is defined as:\n",
-        "$$\n",
-        "\\tilde{A} = \\hat{D}^{-\\frac{1}{2}}\\hat{A}\\hat{D}^{-\\frac{1}{2}}\n",
-        "$$\n",
+        "$$\\tilde{A} = \\hat{D}^{-\\frac{1}{2}}\\hat{A}\\hat{D}^{-\\frac{1}{2}}$$",
         "with $\\hat{A} = A + I$, where $A$ denotes the adjacency matrix and $I$ denotes the identity matrix, $\\hat{D}$ refers to the diagonal node degree matrix of $\\hat{A}$."
       ],
       "metadata": {
@@ -5756,4 +5754,4 @@
       }
     }
   ]
-}
+}
\ No newline at end of file

notebooks/sparse/hgnn.ipynb

@@ -186,11 +186,9 @@
         "\n",
         "## Hypergraph Neural Network (HGNN) Layer\n",
         "\n",
-        "The **[HGNN layer](https://arxiv.org/pdf/1809.09401.pdf)** is defined as:\n",
-        "$$\n",
-        "f(X^{(l)}, H; W^{(l)}) = \\sigma(L X^{(l)} W^{(l)}) \\\\\n",
-        "L = D_v^{-1/2} H B D_e^{-1} H^\\top D_v^{-1/2}\n",
-        "$$\n",
+        "The [HGNN layer](https://arxiv.org/pdf/1809.09401.pdf) is defined as:\n",
+        "$$f(X^{(l)}, H; W^{(l)}) = \\sigma(L X^{(l)} W^{(l)})$$",
+        "$$L = D_v^{-1/2} H B D_e^{-1} H^\\top D_v^{-1/2}$$",
         "where\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",
@@ -401,11 +399,11 @@
     {
       "cell_type": "markdown",
       "source": [
-        "For the complete example of HGNN, please refer to https://github.com/dmlc/dgl/blob/master/examples/sparse/hgnn.py."
+        "For the complete example of HGNN, please refer to [here](https://github.com/dmlc/dgl/blob/master/examples/sparse/hgnn.py)."
       ],
       "metadata": {
         "id": "59pCzjpBOyEW"
       }
     }
   ]
-}
+}
\ No newline at end of file