address concerns

docs/_build/html/_sources/index.rst.txt

@@ -154,7 +154,7 @@ Contact
 
 We welcome your contributions! If you’d like to contribute your datasets or algorithms, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.
 
-For a new dataset, please upload it to the https://github.com/xianggebenben/GraphSL/tree/main/data folder. The file should be a dictionary object saved by pickle(https://docs.python.org/3/library/pickle.html). It contains a key "adj_mat" with the value of a garph adjacency matrix (sprase numpy array with the CSR(https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) format).
+For a new dataset, please upload it to the data(https://github.com/xianggebenben/GraphSL/tree/main/data) folder. The file should be a dictionary object saved by pickle(https://docs.python.org/3/library/pickle.html). It contains a key "adj_mat" with the value of a garph adjacency matrix (sprase numpy array with the CSR(https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) format).
 
 For a new algorithm, please determine it whether it belongs to presribed methods or GNN-based methods: if it belongs to the prescribed methods,  add your algorithm as a new class in the GraphSL/Prescribed.py(https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Prescribed.py). Otherwises, please upload it as a folder under the GraphSL/GNN(https://github.com/xianggebenben/GraphSL/tree/main/GraphSL/GNN) folder. Typically, the algorithm should include a "train" function and a "test" function, and the "test" function should return a Metric object(https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Evaluation.py).
 

docs/_build/html/index.html

@@ -208,7 +208,7 @@ <h2>Usage<a class="headerlink" href="#usage" title="Link to this heading"></a
 <section id="contact">
 <h2>Contact<a class="headerlink" href="#contact" title="Link to this heading"></a></h2>
 <p>We welcome your contributions! If you’d like to contribute your datasets or algorithms, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.</p>
-<p>For a new dataset, please upload it to the <a class="reference external" href="https://github.com/xianggebenben/GraphSL/tree/main/data">https://github.com/xianggebenben/GraphSL/tree/main/data</a> folder. The file should be a dictionary object saved by pickle(<a class="reference external" href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a>). It contains a key “adj_mat” with the value of a garph adjacency matrix (sprase numpy array with the CSR(<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html</a>) format).</p>
+<p>For a new dataset, please upload it to the data(<a class="reference external" href="https://github.com/xianggebenben/GraphSL/tree/main/data">https://github.com/xianggebenben/GraphSL/tree/main/data</a>) folder. The file should be a dictionary object saved by pickle(<a class="reference external" href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a>). It contains a key “adj_mat” with the value of a garph adjacency matrix (sprase numpy array with the CSR(<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html</a>) format).</p>
 <p>For a new algorithm, please determine it whether it belongs to presribed methods or GNN-based methods: if it belongs to the prescribed methods,  add your algorithm as a new class in the GraphSL/Prescribed.py(<a class="reference external" href="https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Prescribed.py">https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Prescribed.py</a>). Otherwises, please upload it as a folder under the GraphSL/GNN(<a class="reference external" href="https://github.com/xianggebenben/GraphSL/tree/main/GraphSL/GNN">https://github.com/xianggebenben/GraphSL/tree/main/GraphSL/GNN</a>) folder. Typically, the algorithm should include a “train” function and a “test” function, and the “test” function should return a Metric object(<a class="reference external" href="https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Evaluation.py">https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Evaluation.py</a>).</p>
 <p>Feel free to Email me (<a class="reference external" href="mailto:junxiang&#46;wang&#37;&#52;&#48;alumni&#46;emory&#46;edu">junxiang<span>&#46;</span>wang<span>&#64;</span>alumni<span>&#46;</span>emory<span>&#46;</span>edu</a>) if you have any questions. Bug reports and feedback can be directed to the Github issues page(<a class="reference external" href="https://github.com/xianggebenben/GraphSL/issues">https://github.com/xianggebenben/GraphSL/issues</a>).</p>
 </section>

docs/index.rst

@@ -154,7 +154,7 @@ Contact
 
 We welcome your contributions! If you’d like to contribute your datasets or algorithms, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.
 
-For a new dataset, please upload it to the https://github.com/xianggebenben/GraphSL/tree/main/data folder. The file should be a dictionary object saved by pickle(https://docs.python.org/3/library/pickle.html). It contains a key "adj_mat" with the value of a garph adjacency matrix (sprase numpy array with the CSR(https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) format).
+For a new dataset, please upload it to the data(https://github.com/xianggebenben/GraphSL/tree/main/data) folder. The file should be a dictionary object saved by pickle(https://docs.python.org/3/library/pickle.html). It contains a key "adj_mat" with the value of a garph adjacency matrix (sprase numpy array with the CSR(https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) format).
 
 For a new algorithm, please determine it whether it belongs to presribed methods or GNN-based methods: if it belongs to the prescribed methods,  add your algorithm as a new class in the GraphSL/Prescribed.py(https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Prescribed.py). Otherwises, please upload it as a folder under the GraphSL/GNN(https://github.com/xianggebenben/GraphSL/tree/main/GraphSL/GNN) folder. Typically, the algorithm should include a "train" function and a "test" function, and the "test" function should return a Metric object(https://github.com/xianggebenben/GraphSL/blob/main/GraphSL/Evaluation.py).