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Simplicial Paths Lifting (Graph to Combinatorial)
A strong inductive bias for deep learning models is processing signals while respecting the local structure of their underlying space. Many real-world systems operate on asymmetric relational structures, leading to directed graph representations. However, most graph and topological models forcibly symmetrize these relationships, thereby losing critical information. While some graph neural networks have recently started incorporating asymmetric pairwise interactions, extending the topological deep learning (TDL) framework to account for asymmetric higher-order relationships remains unexplored.
Recent studies have examined cascading dynamics on networks at the simplicial level [2]. In Topological Data Analysis (TDA), the use of topological tools to address questions in neuroscience has generated interest in constructing topological spaces from digraphs to better understand the phenomena they support [3].
For this reason, we suggest using maximal simplicial paths, deerived from directed graphs, as cell of a combinatorial complex. Therefore, we are proposing a lifting from directed graphs to combinatorial complexes.
Next, we provide an introduction to the fundamental concepts underlying our approach. For a more comprehensive exploration of these basics, we refer the reader to [1]. To the best of our knowledge, this is the first lifting taking into account an higher-order notion of directionality in defining cells, differently from, e.g., taking directly as cells the simplices of a directed flag complex (see below).
Directed Graphs
A directed graph (digraph) is a pair
Abstract Simplicial Complexes
An abstract simplicial complex is a pair
There is a standard way of building an abstract simplicial complex from a graph.
Flag Complex
Given a graph
The following are the natural generalization of flag complexes for digraphs.
Directed Flag Complex
An ordered
Edge paths on digraphs
A path on a digraph is a sequence
Directed cliques have an inherent directionality, which we exploit to extend the notion to higher-dimensional simplicial paths formed by sequences of simplices in the directed flag complex.
We will impose the notion of direction via face maps.
Face maps
Face maps uniquely identify the faces of the simplex by omitting the $i$th-vertex. Let
$$ \hat{d}_i(\sigma) = \begin{cases} (v_0, \ldots, \hat{v}i, \ldots, v_n) & \text{if } i < n, \ (v_0, \ldots, v{n-1}, \hat{v}_n) & \text{if } i \geq n. \end{cases} $$
Directed Q-Connectivity
For an ordered simplicial complex
-
$\sigma \leftrightarrow \tau$ , -
$\hat{d}_i(\sigma) \leftrightarrow \alpha \leftrightarrow \hat{d}_j(\tau)$ , for some$q$ -simplex$\alpha \in K$ .
By closing the directed q-nearness transitively, the ordered pair
such that any two consecutive ones are
Theorem The relation of being
Directions and Simplicial Paths as Topological Information
Instead of focusing on the path structure of the digraph, we look at the path structure of the high-dimensional simplices by exploring the
Different choices of
The
[1] Henri Riihïmaki. Simplicial q-Connectivity of Directed Graphs with Applications to Network Analysis.
[2] Bengier Ulgen, Dane Taylor. Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes.
[3] Dane Taylor, Florian Klimm. Topological data analysis of contagion maps for examining spreading processes on networks
[4] D. Lütgehetmann, D. Govc, J.P. Smith, and R. Levi. Computing persistent homology of directed flag complexes.
From https://github.com/pyt-team/challenge-icml-2024/pull/57
- Defining GCCNs
- Defining backbone models
- Reproducing experiments
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Graph to Simplicial Complex
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Graph to Cell Complex
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Graph to Hypergraph
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Graph to Combinatorial
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Pointcloud to Graph
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Pointcloud to Simplicial
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Pointcloud to Hypergraph
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Hypergraph to Simplicial
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Hypergraph to Combinatorial