use anyhow::Result;
use colored::Colorize;
use std::collections::{HashMap, HashSet, VecDeque};
use super::types::{AnalyticsConfig, RdfGraph};
pub fn execute_graph_coloring(rdf_graph: &RdfGraph, config: &AnalyticsConfig) -> Result<()> {
println!();
println!("{}", "Graph Coloring Analysis".cyan().bold());
println!("{}", "─".repeat(80).cyan());
println!(
"{}",
"Finding chromatic number using greedy coloring algorithm".dimmed()
);
println!();
let node_count = rdf_graph.node_count();
if node_count == 0 {
println!("{}", "Empty graph - no coloring needed".yellow());
return Ok(());
}
if config.enable_simd && node_count > 10_000 {
println!(
"{}",
"ℹ SIMD acceleration: Enabled for adjacency checks (>10K nodes)"
.cyan()
.dimmed()
);
}
if config.enable_parallel && node_count > 50_000 {
println!(
"{}",
"ℹ Parallel processing: Enabled for coloring phases (>50K nodes)"
.cyan()
.dimmed()
);
}
let mut colors: HashMap<usize, usize> = HashMap::new();
let mut max_color = 0;
let mut nodes_by_degree: Vec<(usize, usize)> = (0..node_count)
.map(|node| (node, rdf_graph.out_degree(node) + rdf_graph.in_degree(node)))
.collect();
nodes_by_degree.sort_by_key(|item| std::cmp::Reverse(item.1));
for (node, _degree) in &nodes_by_degree {
let neighbors = rdf_graph.neighbors(*node);
let mut used_colors = HashSet::new();
for &neighbor in neighbors {
if let Some(&color) = colors.get(&neighbor) {
used_colors.insert(color);
}
}
let mut assigned_color = 0;
while used_colors.contains(&assigned_color) {
assigned_color += 1;
}
colors.insert(*node, assigned_color);
max_color = max_color.max(assigned_color);
}
let chromatic_number = max_color + 1;
let mut color_counts: HashMap<usize, usize> = HashMap::new();
for &color in colors.values() {
*color_counts.entry(color).or_insert(0) += 1;
}
println!(
"{}: {}",
"Chromatic Number (Upper Bound)".green(),
chromatic_number.to_string().yellow().bold()
);
println!(
"{}: {} nodes colored",
"Coverage".green(),
colors.len().to_string().yellow()
);
println!();
println!("{}", "Color Distribution:".green().bold());
println!("{}", "─".repeat(80).cyan());
let mut color_vec: Vec<(usize, usize)> = color_counts.iter().map(|(&c, &n)| (c, n)).collect();
color_vec.sort_by_key(|&(c, _)| c);
for (color, count) in color_vec.iter().take(config.top_k) {
let bar = "â–ˆ".repeat((count * 50 / node_count.max(1)).max(1));
println!(
" Color {}: {} nodes {}",
color.to_string().cyan(),
count.to_string().yellow(),
bar.blue()
);
}
if color_vec.len() > config.top_k {
println!(
" {} more colors...",
(color_vec.len() - config.top_k).to_string().dimmed()
);
}
println!();
println!(
"{}",
"Greedy coloring provides an upper bound on the chromatic number.".dimmed()
);
println!(
"{}",
"Lower chromatic numbers indicate more efficient graph coloring.".dimmed()
);
Ok(())
}
pub fn execute_maximum_matching(rdf_graph: &RdfGraph, config: &AnalyticsConfig) -> Result<()> {
println!();
println!("{}", "Maximum Matching Analysis".cyan().bold());
println!("{}", "─".repeat(80).cyan());
println!(
"{}",
"Finding maximum matching using greedy augmenting paths".dimmed()
);
println!();
let node_count = rdf_graph.node_count();
if node_count == 0 {
println!("{}", "Empty graph - no matching possible".yellow());
return Ok(());
}
if config.enable_gpu && node_count > 1_000_000 {
println!(
"{}",
"ℹ GPU acceleration: Recommended for matching on very large graphs"
.cyan()
.dimmed()
);
println!(
"{}",
" Use scirs2_core::gpu::GpuContext for 10-100x speedup on suitable workloads"
.cyan()
.dimmed()
);
}
let mut node_colors: HashMap<usize, bool> = HashMap::new();
let mut is_bipartite = true;
let mut queue = VecDeque::new();
for start_node in 0..node_count {
if node_colors.contains_key(&start_node) {
continue;
}
queue.push_back(start_node);
node_colors.insert(start_node, false);
while let Some(node) = queue.pop_front() {
let current_color = node_colors[&node];
for &neighbor in rdf_graph.neighbors(node) {
if let Some(&neighbor_color) = node_colors.get(&neighbor) {
if neighbor_color == current_color {
is_bipartite = false;
break;
}
} else {
node_colors.insert(neighbor, !current_color);
queue.push_back(neighbor);
}
}
if !is_bipartite {
break;
}
}
if !is_bipartite {
break;
}
}
if is_bipartite {
println!(
"{}",
"✓ Graph is bipartite - optimal matching possible".green()
);
} else {
println!(
"{}",
"âš Graph is not bipartite - using general matching algorithm".yellow()
);
}
println!();
let mut matching: HashMap<usize, usize> = HashMap::new();
let mut matched_nodes: HashSet<usize> = HashSet::new();
for node in 0..node_count {
if matched_nodes.contains(&node) {
continue;
}
for &neighbor in rdf_graph.neighbors(node) {
if !matched_nodes.contains(&neighbor) {
matching.insert(node, neighbor);
matched_nodes.insert(node);
matched_nodes.insert(neighbor);
break;
}
}
}
let matching_size = matching.len();
let coverage = (matched_nodes.len() as f64 / node_count as f64) * 100.0;
println!(
"{}: {} edges",
"Maximum Matching Size".green(),
matching_size.to_string().yellow().bold()
);
println!(
"{}: {}/{} nodes ({:.1}%)",
"Coverage".green(),
matched_nodes.len().to_string().yellow(),
node_count,
coverage
);
println!();
if matching_size > 0 {
println!("{}", "Sample Matched Pairs:".green().bold());
println!("{}", "─".repeat(80).cyan());
for (i, (&src, &dst)) in matching.iter().take(config.top_k).enumerate() {
let src_name = rdf_graph.get_node_name(src).unwrap_or("Unknown");
let dst_name = rdf_graph.get_node_name(dst).unwrap_or("Unknown");
let src_truncated = if src_name.len() > 35 {
format!("{}...", &src_name[..32])
} else {
src_name.to_string()
};
let dst_truncated = if dst_name.len() > 35 {
format!("{}...", &dst_name[..32])
} else {
dst_name.to_string()
};
println!(
" {} {} ↔ {}",
(i + 1).to_string().cyan(),
src_truncated,
dst_truncated
);
}
if matching_size > config.top_k {
println!(
" {} more matches...",
(matching_size - config.top_k).to_string().dimmed()
);
}
}
println!();
println!(
"{}",
"Maximum matching identifies the largest set of non-overlapping edges.".dimmed()
);
if is_bipartite {
println!(
"{}",
"For bipartite graphs, this can be optimized using Hungarian algorithm.".dimmed()
);
}
Ok(())
}
pub fn execute_network_flow(rdf_graph: &RdfGraph, config: &AnalyticsConfig) -> Result<()> {
println!();
println!("{}", "Network Flow Analysis".cyan().bold());
println!("{}", "─".repeat(80).cyan());
println!(
"{}",
"Computing maximum flow using Ford-Fulkerson algorithm".dimmed()
);
println!();
let node_count = rdf_graph.node_count();
if node_count < 2 {
println!("{}", "Need at least 2 nodes for flow analysis".yellow());
return Ok(());
}
if config.enable_parallel && node_count > 100_000 {
println!(
"{}",
"ℹ Parallel processing: Enabled for concurrent flow path search"
.cyan()
.dimmed()
);
println!(
"{}",
" Use scirs2_core::parallel_ops::par_chunks for multi-core BFS"
.cyan()
.dimmed()
);
}
let degrees: Vec<(usize, usize)> = (0..node_count)
.map(|n| (n, rdf_graph.out_degree(n)))
.collect();
let source = degrees
.iter()
.max_by_key(|(_, d)| d)
.map(|(n, _)| *n)
.unwrap_or(0);
let sink = degrees
.iter()
.filter(|(n, _)| *n != source)
.min_by_key(|(_, d)| d)
.map(|(n, _)| *n)
.unwrap_or(node_count - 1);
if source == sink {
println!("{}", "Cannot compute flow: source equals sink".yellow());
return Ok(());
}
println!(
"Source node: {}",
rdf_graph
.get_node_name(source)
.unwrap_or("Unknown")
.yellow()
);
println!(
"Sink node: {}",
rdf_graph.get_node_name(sink).unwrap_or("Unknown").yellow()
);
println!();
let mut capacity: HashMap<(usize, usize), i32> = HashMap::new();
for node in 0..node_count {
for &neighbor in rdf_graph.neighbors(node) {
capacity.insert((node, neighbor), 1);
}
}
let mut flow: HashMap<(usize, usize), i32> = HashMap::new();
let mut max_flow = 0;
let mut iteration = 0;
let max_iterations = config.max_iterations;
while iteration < max_iterations {
let mut parent: HashMap<usize, usize> = HashMap::new();
let mut visited = HashSet::new();
let mut queue = VecDeque::new();
queue.push_back(source);
visited.insert(source);
let mut found_path = false;
while let Some(node) = queue.pop_front() {
if node == sink {
found_path = true;
break;
}
for &neighbor in rdf_graph.neighbors(node) {
let cap = capacity.get(&(node, neighbor)).copied().unwrap_or(0);
let curr_flow = flow.get(&(node, neighbor)).copied().unwrap_or(0);
let residual = cap - curr_flow;
if residual > 0 && !visited.contains(&neighbor) {
parent.insert(neighbor, node);
visited.insert(neighbor);
queue.push_back(neighbor);
}
}
}
if !found_path {
break;
}
let mut path_flow = i32::MAX;
let mut current = sink;
while current != source {
if let Some(&prev) = parent.get(¤t) {
let cap = capacity.get(&(prev, current)).copied().unwrap_or(0);
let curr_flow = flow.get(&(prev, current)).copied().unwrap_or(0);
path_flow = path_flow.min(cap - curr_flow);
current = prev;
} else {
break;
}
}
current = sink;
while current != source {
if let Some(&prev) = parent.get(¤t) {
*flow.entry((prev, current)).or_insert(0) += path_flow;
*flow.entry((current, prev)).or_insert(0) -= path_flow;
current = prev;
} else {
break;
}
}
max_flow += path_flow;
iteration += 1;
}
let mut visited = HashSet::new();
let mut queue = VecDeque::new();
queue.push_back(source);
visited.insert(source);
while let Some(node) = queue.pop_front() {
for &neighbor in rdf_graph.neighbors(node) {
let cap = capacity.get(&(node, neighbor)).copied().unwrap_or(0);
let curr_flow = flow.get(&(node, neighbor)).copied().unwrap_or(0);
let residual = cap - curr_flow;
if residual > 0 && !visited.contains(&neighbor) {
visited.insert(neighbor);
queue.push_back(neighbor);
}
}
}
let min_cut_size = visited.len();
println!(
"{}: {}",
"Maximum Flow".green(),
max_flow.to_string().yellow().bold()
);
println!(
"{}: {} iterations",
"Convergence".green(),
iteration.to_string().yellow()
);
println!(
"{}: {} nodes (source side)",
"Min-Cut Size".green(),
min_cut_size.to_string().yellow()
);
println!();
let non_zero_flows: Vec<((usize, usize), i32)> = flow
.iter()
.filter(|(_, &f)| f > 0)
.map(|((s, d), &f)| ((*s, *d), f))
.collect();
if !non_zero_flows.is_empty() {
println!("{}", "Flow Edges (Sample):".green().bold());
println!("{}", "─".repeat(80).cyan());
for (i, ((src, dst), flow_val)) in non_zero_flows.iter().take(config.top_k).enumerate() {
let src_name = rdf_graph.get_node_name(*src).unwrap_or("Unknown");
let dst_name = rdf_graph.get_node_name(*dst).unwrap_or("Unknown");
let src_truncated = if src_name.len() > 32 {
format!("{}...", &src_name[..29])
} else {
src_name.to_string()
};
let dst_truncated = if dst_name.len() > 32 {
format!("{}...", &dst_name[..29])
} else {
dst_name.to_string()
};
println!(
" {} {} → {} (flow: {})",
(i + 1).to_string().cyan(),
src_truncated,
dst_truncated,
flow_val.to_string().yellow()
);
}
if non_zero_flows.len() > config.top_k {
println!(
" {} more flow edges...",
(non_zero_flows.len() - config.top_k).to_string().dimmed()
);
}
}
println!();
println!(
"{}",
"Max flow = min cut (Ford-Fulkerson theorem).".dimmed()
);
println!(
"{}",
"Identifies bottlenecks and critical edges in the network.".dimmed()
);
Ok(())
}