Network topology edge parsing

network-topology-edge-parsing

Summary

Parse a directed edge list from a two-column text file into an adjacency structure to initialize a global metabolic network for FNICM core-node identification. This skill bridges raw edge data into a computable digraph representation.

When to use

Apply this skill when you have a metabolic or biological network encoded as pairwise directed interactions (e.g., A→B enzyme–substrate relationships, metabolite conversions, or regulatory edges) in plain-text format, and you need to construct an initial global network topology before trust-score propagation and subnetwork analysis.

When NOT to use

• The input is already a serialized graph object (e.g., adjacency matrix, .gml, .graphml, or NetworkX pickle); skip directly to trust-score input.
• The network is undirected or edge directionality is irrelevant to your analysis; simpler symmetric adjacency parsing may be preferable.
• Your input is a metabolite concentration matrix or feature table rather than a network topology; use dimensionality reduction or correlation-based network inference first.

Inputs

• digraph edge list file (.txt format, tab- or space-delimited, two columns A and B)

Outputs

• parsed adjacency structure (adjacency list or matrix representation of directed graph)
• validated node set (all unique nodes extracted from A and B columns)

How to apply

Load the input .txt file containing exactly two columns (A and B), where each row represents a directed edge from source node A to target node B. Parse each row into an adjacency structure (e.g., adjacency list or matrix) that preserves directionality. Validate that all node identifiers are consistently formatted and non-empty. The resulting adjacency structure serves as the foundation for FNICM's trust-score propagation algorithm, which requires accurate representation of the network's topology to compute node centrality and identify statistically significant perturbations. Ensure no self-loops or duplicate edges are inadvertently introduced during parsing, as these can bias downstream trust ranking.

Related tools

• FNICM (downstream consumer of parsed digraph for trust-score propagation and core-node ranking) — https://github.com/LiQi94/FNICM

Evaluation signals

• Adjacency structure has |E| entries where E is the number of edge rows, with no duplicate or reversed edges unless intentional.
• All nodes appearing in column B have corresponding entries accessible from the adjacency structure (i.e., no orphaned target nodes).
• Node count equals the unique values in columns A and B combined; validate against expected metabolite or enzyme cardinality.
• Directionality is preserved: edge (A, B) is not equivalent to (B, A) in the adjacency representation.
• No parsing errors (e.g., malformed rows, non-ASCII characters, or misaligned column counts) are logged; file integrity check passes.

Limitations

• The skill assumes the input file is well-formed; malformed or inconsistent delimiter usage (mixing tabs and spaces) may cause parsing failures.
• Self-loops and duplicate edges are not filtered automatically; manual validation or preprocessing may be required if the source data contains redundancies.
• Large networks (>>10,000 nodes) may require memory-efficient data structures (e.g., sparse adjacency lists); dense matrix representations could exceed RAM.
• The skill does not validate biological plausibility or reconcile conflicting edge definitions (e.g., A→B from one source and B→A from another); domain-specific curation is necessary.
• FNICM is compatible exclusively with the Windows operating system, limiting reproducibility on Linux or macOS systems.

Evidence

• [readme] you need to input a digraph list file in a .txt format, which contains two columns of data, A and B. There is an edge from column A to column B.: "you need to input a digraph list file in a .txt format, which contains two columns of data, A and B. There is an edge from column A to column B."
• [readme] Note that this is an initial global network, and the subsequent subnetwork is constructed based on this network.: "Note that this is an initial global network, and the subsequent subnetwork is constructed based on this network."
• [other] Load the digraph edge list from the input .txt file (two columns A and B representing directed edges from A to B) and parse into an adjacency structure.: "Load the digraph edge list from the input .txt file (two columns A and B representing directed edges from A to B) and parse into an adjacency structure."
• [other] FNICM requires a digraph list input file in .txt format with two columns (A and B) where each row represents a directed edge from node A to node B, forming the initial global network upon which subsequent subnetwork construction is based.: "FNICM requires a digraph list input file in .txt format with two columns (A and B) where each row represents a directed edge from node A to node B"