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Update index.rst: Fix tables #55

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274 changes: 158 additions & 116 deletions docs/index.rst
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Expand Up @@ -24,67 +24,74 @@ We list the neural networks trained and evaluated by `TopoBenchmarkX`, organized

Graphs
******
.. list-table::

+-------+----------------------------------------------------+
| Model | Reference |
+=======+====================================================+
| GAT | `Graph Attention Networks <https://openreview.net/pdf?id=rJXMpikCZ>`_ |
+-------+----------------------------------------------------+
| GIN | `How Powerful are Graph Neural Networks? <https://openreview.net/pdf?id=ryGs6iA5Km>`_ |
+-------+----------------------------------------------------+
| GCN | `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907v4>`_ |
+-------+----------------------------------------------------+
.. list-table::
:widths: 20 80
:header-rows: 1

* - Model
- Reference
* - GAT
- `Graph Attention Networks <https://openreview.net/pdf?id=rJXMpikCZ>`_
* - GIN
- `How Powerful are Graph Neural Networks? <https://openreview.net/pdf?id=ryGs6iA5Km>`_
* - GCN
- `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907v4>`_

Simplicial complexes
********************
.. list-table::
.. list-table::
:widths: 20 80
:header-rows: 1

* - Model
- Reference
* - SAN
- `Simplicial Attention Neural Networks <https://arxiv.org/pdf/2203.07485>`_
* - SCCN
- `Efficient Representation Learning for Higher-Order Data with Simplicial Complexes <https://openreview.net/pdf?id=nGqJY4DODN>`_
* - SCCNN
- `Convolutional Learning on Simplicial Complexes <https://arxiv.org/pdf/2301.11163>`_
* - SCN
- `Simplicial Complex Neural Networks <https://ieeexplore.ieee.org/document/10285604>`_

+-------+----------------------------------------------------+
| Model | Reference |
+=======+====================================================+
| SAN | `Simplicial Attention Neural Networks <https://arxiv.org/pdf/2203.07485>`_ |
+-------+----------------------------------------------------+
| SCCN | `Efficient Representation Learning for Higher-Order Data with Simplicial Complexes <https://openreview.net/pdf?id=nGqJY4DODN>`_ |
+-------+----------------------------------------------------+
| SCCNN | `Convolutional Learning on Simplicial Complexes <https://arxiv.org/pdf/2301.11163>`_ |
+-------+----------------------------------------------------+
| SCN | `Simplicial Complex Neural Networks <https://ieeexplore.ieee.org/document/10285604>`_ |
+-------+----------------------------------------------------+

Cellular complexes
******************
.. list-table::
:widths: 20 80
:header-rows: 1

* - Model
- Reference
* - CAN
- `Cell Attention Network <https://arxiv.org/pdf/2209.08179>`_
* - CCCN
- `A learning algorithm for computational connected cellular network <https://ieeexplore.ieee.org/document/1202221>`_
* - CXN
- `Cell Complex Neural Networks <https://openreview.net/pdf?id=6Tq18ySFpGU>`_
* - CWN
- `Weisfeiler and Lehman Go Cellular: CW Networks <https://arxiv.org/pdf/2106.12575>`_

+-------+----------------------------------------------------+
| Model | Reference |
+=======+====================================================+
| CAN | `Cell Attention Network <https://arxiv.org/pdf/2209.08179>`_ |
+-------+----------------------------------------------------+
| CCCN | `A learning algorithm for computational connected cellular network <https://ieeexplore.ieee.org/document/1202221>`_ |
+-------+----------------------------------------------------+
| CXN | `Cell Complex Neural Networks <https://openreview.net/pdf?id=6Tq18ySFpGU>`_ |
+-------+----------------------------------------------------+
| CWN | `Weisfeiler and Lehman Go Cellular: CW Networks <https://arxiv.org/pdf/2106.12575>`_ |
+-------+----------------------------------------------------+

Hypergraphs
***********
.. list-table::
:widths: 20 80
:header-rows: 1

* - Model
- Reference
* - AllDeepSet
- `You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks <https://openreview.net/pdf?id=hpBTIv2uy_E>`_
* - AllSetTransformer
- `You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks <https://openreview.net/pdf?id=hpBTIv2uy_E>`_
* - EDGNN
- `Equivariant Hypergraph Diffusion Neural Operators <https://arxiv.org/pdf/2207.06680>`_
* - UniGNN
- `UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks <https://arxiv.org/pdf/2105.00956>`_
* - UniGNN2
- `UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks <https://arxiv.org/pdf/2105.00956>`_

+----------------+----------------------------------------------------+
| Model | Reference |
+================+====================================================+
| AllDeepSet | `You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks <https://openreview.net/pdf?id=hpBTIv2uy_E>`_ |
+----------------+----------------------------------------------------+
| AllSetTransformer | `You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks <https://openreview.net/pdf?id=hpBTIv2uy_E>`_ |
+----------------+----------------------------------------------------+
| EDGNN | `Equivariant Hypergraph Diffusion Neural Operators <https://arxiv.org/pdf/2207.06680>`_ |
+----------------+----------------------------------------------------+
| UniGNN | `UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks <https://arxiv.org/pdf/2105.00956>`_ |
+----------------+----------------------------------------------------+
| UniGNN2 | `UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks <https://arxiv.org/pdf/2105.00956>`_ |
+----------------+----------------------------------------------------+

🚀 Liftings
-----------
Expand All @@ -93,93 +100,128 @@ We list the liftings used in `TopoBenchmarkX` to transform datasets. Here, a *li

Graph2Simplicial
****************

.. list-table::

+----------------+--------------------------------------------------------------------------------------------------------------------+
| Name | Description |
+================+====================================================================================================================+
| CliqueLifting | The algorithm finds the cliques in the graph and creates simplices. Given a clique the first simplex added is the |
| | one containing all the nodes of the clique, then the simplices composed of all the possible combinations with one |
| | node missing, then two nodes missing, and so on, until all the possible pairs are added. Then the method moves to |
| | the next clique. |
+----------------+--------------------------------------------------------------------------------------------------------------------+
| KHopLifting | For each node in the graph, take the set of its neighbors, up to k distance, and the node itself. These sets are |
| | then treated as simplices. The dimension of each simplex depends on the degree of the nodes. For example, a node |
| | with d neighbors forms a d-simplex. |
+----------------+--------------------------------------------------------------------------------------------------------------------+

:widths: 20 60 20
:header-rows: 1

* - Name
- Description
- Reference
* - CliqueLifting
- The algorithm finds the cliques in the graph and creates simplices. Given a clique the first simplex added is the one containing all the nodes of the clique, then the simplices composed of all the possible combinations with one node missing, then two nodes missing, and so on, until all the possible pairs are added. Then the method moves to the next clique.
- `Simplicial Complexes <https://en.wikipedia.org/wiki/Clique_complex>`_
* - KHopLifting
- For each node in the graph, take the set of its neighbors, up to k distance, and the node itself. These sets are then treated as simplices. The dimension of each simplex depends on the degree of the nodes. For example, a node with d neighbors forms a d-simplex.
- `Neighborhood Complexes <https://arxiv.org/pdf/math/0512077>`_

Graph2Cell
**********

.. list-table::
:widths: 20 60 20
:header-rows: 1

* - Name
- Description
- Reference
* - CellCycleLifting
- To lift a graph to a cell complex (CC) we proceed as follows. First, we identify a finite set of cycles (closed loops) within the graph. Second, each identified cycle in the graph is associated to a 2-cell, such that the boundary of the 2-cell is the cycle. The nodes and edges of the cell complex are inherited from the graph.
- `Appendix B <https://arxiv.org/abs/2206.00606>`_

+------------------+--------------------------------------------------------------------------------------------------------------------+
| Name | Description |
+==================+====================================================================================================================+
| CellCycleLifting | To lift a graph to a cell complex (CC) we proceed as follows. First, we identify a finite set of cycles (closed loops) within the graph. Second, each identified cycle in the graph is associated to a 2-cell, such that the boundary of the 2-cell is the cycle. The nodes and edges of the cell complex are inherited from the graph. | ` Appendix B <https://arxiv.org/abs/2206.00606>`_ |
+------------------+--------------------------------------------------------------------------------------------------------------------+

Graph2Hypergraph
****************

.. list-table::
:widths: 20 60 20
:header-rows: 1

* - Name
- Description
- Reference
* - KHopLifting
- For each node in the graph, the algorithm finds the set of nodes that are at most k connections away from the initial node. This set is then used to create a hyperedge. The process is repeated for all nodes in the graph.
- `Section 3.4 <https://ieeexplore.ieee.org/abstract/document/9264674>`_
* - KNearestNeighborsLifting
- For each node in the graph, the method finds the k nearest nodes by using the Euclidean distance between the vectors of features. The set of k nodes found is considered as a hyperedge. The process is repeated for all nodes in the graph.
- `Section 3.1 <https://ieeexplore.ieee.org/abstract/document/9264674>`_

+------------------------+--------------------------------------------------------------------------------------------------------------------+
| Name | Description |
+========================+====================================================================================================================+
| KHopLifting | For each node in the graph, the algorithm finds the set of nodes that are at most k connections away from the |
| | initial node. This set is then used to create a hyperedge. The process is repeated for all nodes in the graph. |
| | `Section 3.4 <https://ieeexplore.ieee.org/abstract/document/9264674>`_ |
+------------------------+--------------------------------------------------------------------------------------------------------------------+
| KNearestNeighborsLifting| For each node in the graph, the method finds the k nearest nodes by using the Euclidean distance between the vectors |
| | of features. The set of k nodes found is considered as a hyperedge. The process is repeated for all nodes in the |
| | graph. |
| | `Section 3.1 <https://ieeexplore.ieee.org/abstract/document/9264674)>`_ |
+------------------------+--------------------------------------------------------------------------------------------------------------------+

📚 Datasets
-----------

.. list-table::

+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Dataset | Task | Description | Reference |
+==========+===================+=========================================+====================================================+
| Cora | Classification | Cocitation dataset. | `Source <https://link.springer.com/article/10.1023/A:1009953814988>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Citeseer | Classification | Cocitation dataset. | `Source <https://dl.acm.org/doi/10.1145/276675.276685>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Pubmed | Classification | Cocitation dataset. | `Source <https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2157>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| MUTAG | Classification | Graph-level classification. | `Source <https://pubs.acs.org/doi/abs/10.1021/jm00106a046>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| PROTEINS | Classification | Graph-level classification. | `Source <https://academic.oup.com/bioinformatics/article/21/suppl_1/i47/202991>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| NCI1 | Classification | Graph-level classification. | `Source <https://ieeexplore.ieee.org/document/4053093>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| NCI109 | Classification | Graph-level classification. | `Source <https://arxiv.org/pdf/2007.08663>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| IMDB-BIN | Classification | Graph-level classification. | `Source <https://dl.acm.org/doi/10.1145/2783258.2783417>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| IMDB-MUL | Classification | Graph-level classification. | `Source <https://dl.acm.org/doi/10.1145/2783258.2783417>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| REDDIT | Classification | Graph-level classification. | `Source <https://proceedings.neurips.cc/paper_files/paper/2017/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Amazon | Classification | Heterophilic dataset. | `Source <https://arxiv.org/pdf/1205.6233>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Minesweeper | Classification | Heterophilic dataset. | `Source <https://arxiv.org/pdf/2302.11640>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Empire | Classification | Heterophilic dataset. | `Source <https://arxiv.org/pdf/2302.11640>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| Tolokers | Classification | Heterophilic dataset. | `Source <https://arxiv.org/pdf/2302.11640>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| US-county-demos | Regression | In turn each node attribute is used as the target label. | `Source <https://arxiv.org/pdf/2002.08274>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+
| ZINC | Regression | Graph-level regression. | `Source <https://pubs.acs.org/doi/10.1021/ci3001277>`_ |
+----------+-------------------+-----------------------------------------+----------------------------------------------------+

:widths: 15 15 40 30
:header-rows: 1

* - Dataset
- Task
- Description
- Reference
* - Cora
- Classification
- Cocitation dataset.
- `Source <https://link.springer.com/article/10.1023/A:1009953814988>`_
* - Citeseer
- Classification
- Cocitation dataset.
- `Source <https://dl.acm.org/doi/10.1145/276675.276685>`_
* - Pubmed
- Classification
- Cocitation dataset.
- `Source <https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2157>`_
* - MUTAG
- Classification
- Graph-level classification.
- `Source <https://pubs.acs.org/doi/abs/10.1021/jm00106a046>`_
* - PROTEINS
- Classification
- Graph-level classification.
- `Source <https://academic.oup.com/bioinformatics/article/21/suppl_1/i47/202991>`_
* - NCI1
- Classification
- Graph-level classification.
- `Source <https://ieeexplore.ieee.org/document/4053093>`_
* - NCI109
- Classification
- Graph-level classification.
- `Source <https://arxiv.org/pdf/2007.08663>`_
* - IMDB-BIN
- Classification
- Graph-level classification.
- `Source <https://dl.acm.org/doi/10.1145/2783258.2783417>`_
* - IMDB-MUL
- Classification
- Graph-level classification.
- `Source <https://dl.acm.org/doi/10.1145/2783258.2783417>`_
* - REDDIT
- Classification
- Graph-level classification.
- `Source <https://proceedings.neurips.cc/paper_files/paper/2017/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf>`_
* - Amazon
- Classification
- Heterophilic dataset.
- `Source <https://arxiv.org/pdf/1205.6233>`_
* - Minesweeper
- Classification
- Heterophilic dataset.
- `Source <https://arxiv.org/pdf/2302.11640>`_
* - Empire
- Classification
- Heterophilic dataset.
- `Source <https://arxiv.org/pdf/2302.11640>`_
* - Tolokers
- Classification
- Heterophilic dataset.
- `Source <https://arxiv.org/pdf/2302.11640>`_
* - US-county-demos
- Regression
- In turn each node attribute is used as the target label.
- `Source <https://arxiv.org/pdf/2002.08274>`_
* - ZINC
- Regression
- Graph-level regression.
- `Source <https://pubs.acs.org/doi/10.1021/ci3001277>`_


🔍 References
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