In this challenge our goal is to order neurons from the fly connectome in maximally "feedforward" way. The high level motivation is gaining insights into direction of information flow in the brain.
The FlyWire connectome is modeled as a directed graph G=(V,E) where V - nodes - represent neurons, and E - weighted edges - represent synaptic connections. In this challenge, the goal is to "feed forward" sort the nodes of this directed graph so that the sum of edge weights pointing backward is minimized (or in other words, forward pointing edge weights maximized). Specifically, your goal is to produce an order O(V) -> {1, ..., N} that assigns a unique number to each node in V, so that the sum of edge weights u -> v where O(u) < U(v) is maximized.

Data
* Connectome - directed graph (download here)
(Columns: ['Source Node  ID', 'Target Node ID', 'Edge Weight']
First few rows of data:
      Source Node  ID      Target Node ID  Edge Weight
0  720575940629970489  720575940631267655            8
1  720575940629970489  720575940620005542           16
2  720575940629970489  720575940626081319            7
3  720575940629970489  720575940652258806            5
4  720575940629970489  720575940624508563           21)
* Benchmark solution (download here)
(Columns: ['Node ID', 'Order']
First few rows of data:
              Node ID  Order
0  720575940626087322      0
1  720575940611746258      1
2  720575940610412386      2
3  720575940624724919      3
4  720575940626365768      4)
Reference code
The benchmark solution provided above was produced using this library (sfas) (see source code here - self-contained, less than 250 lines including comments). You are welcome to alter the algorithm therein or implement your own.
This is the benchmark solution code:
import random
import pandas as pd

def nodes_from_connections_table(connections_table):
    return set([r[0] for r in connections_table]).union(set([r[1] for r in connections_table]))

def adjacency_lists(connections_table, verbosity=0):
    node_list = nodes_from_connections_table(connections_table)
    input_lists = {n: [] for n in node_list}
    output_lists = {n: [] for n in node_list}
    for r in connections_table:
        input_lists[r[1]].append(r)
        output_lists[r[0]].append(r)
    if verbosity:
        print(f"Nodes: {len(node_list)}, "
              f"max out: {max([len(s) for s in output_lists.values()])}, "
              f"max in: {max([len(s) for s in input_lists.values()])}")
    return input_lists, output_lists


def percentage_str(part, whole):
    return f"{int(100 * float(part) / float(whole))}%"

# Calculates linear arrangement of the nodes that minimizes (greedily) the number of backward edges
# Implements Algorithm 1 from http://www.vldb.org/pvldb/vol10/p133-simpson.pdf
# Input:
#  - connections: list of edges, each represented as a 3-item list consisting of [<from node>, <to node>, <edge weight>]
#  - verbosity: prints progress and other stats for values >0
#  - random_seed: randomness is in picking the next "greedy" step among equally qualified ones
# Output:
#  - list with all nodes, ordered so that the total weight of edges going backwards (w.r.t. this order) is small
def compute_order(connections, verbosity=0, random_seed=0):
    total_edge_weight = sum([r[2] for r in connections])
    if verbosity > 0:
        print(f"Connections: {len(connections)}, total edge weight: {total_edge_weight}")

    # capture adjacency lists for all nodes (from the connections table)
    input_rows, output_rows = adjacency_lists(connections)

    # randomize
    random.seed(random_seed)
    connections = list(connections)
    random.shuffle(connections)
    node_ids = list(input_rows.keys())
    random.shuffle(node_ids)

    # optimization: map node ids to serial ids 0,...,N-1
    nodes_compacted = {k: i for i, k in enumerate(node_ids)}

    # builds a connection dictionary for a single node as follows:
    #  1. maps node ids to compact ids
    #  2. combines parallel edges (summing their weights)
    #  3. return a dictionary, where keys are compacted node ids of neighbors (either outgoing or incoming,
    #     depending on nidx), and values are the weights of edges connecting to them.
    def compact_dict(rows, nidx):
        cd = {}
        for r in rows:
            cn = nodes_compacted[r[nidx]]
            cd[cn] = cd.get(cn, 0) + r[2]
        return cd

    if verbosity > 2:
        print(f"Input adjacency: {input_rows}")
        print(f"Output adjacency: {output_rows}")

    # builds input/output connection dictionaries for all nodes (compacted)
    input_dict = {nodes_compacted[k]: compact_dict(v, 0) for k, v in input_rows.items()}
    output_dict = {nodes_compacted[k]: compact_dict(v, 1) for k, v in output_rows.items()}

    # sanity checks
    for nd, indict in input_dict.items():
        for ind, w in indict.items():
            assert output_dict[ind][nd] == w
    for nd, indict in output_dict.items():
        for ind, w in indict.items():
            assert input_dict[ind][nd] == w

    # this keeps track of the total sum of weights of all edges pointing backwards
    feedback_val = 0

    # Remove anti-parallel edges: we still might have anti-parallel edges of the form $e_1 = u \to v$ and
    # $e_2 = v \to u$ with $w_1$ and $w_2$ their respective weights. Without loss of generality, lets assume
    # $w_1 \leq w_2$; we remove $e_1$ from the graph, and update $w_2 = w_2 - w_1$. And we keep record of
    # the sum of all weights of the removed edges in 'feedback_val'.
    anti_parallel_edges = []
    for nd, indict in input_dict.items():
        for ind, w in indict.items():
            if nd < ind: # add each pair only once
                if nd in input_dict[ind]:
                    anti_parallel_edges.append((ind, nd))
    if anti_parallel_edges:
        print(f"Found {len(anti_parallel_edges)} anti-parallel edges")
        for (n1, n2) in anti_parallel_edges:
            w12 = output_dict[n1][n2]
            assert w12 == input_dict[n2][n1]
            w21 = output_dict[n2][n1]
            assert w21 == input_dict[n1][n2]
            if w12 > w21:
                del output_dict[n2][n1]
                del input_dict[n1][n2]
                output_dict[n1][n2] -= w21
                input_dict[n2][n1] -= w21
                feedback_val += w21
            else:
                del output_dict[n1][n2]
                del input_dict[n2][n1]
                output_dict[n2][n1] -= w12
                input_dict[n1][n2] -= w12
                feedback_val += w12
        print(f"Starting with feedback_val {feedback_val}")

    # sanity checks after removing anti-parallel edges
    for nd, indict in input_dict.items():
        for ind, w in indict.items():
            assert output_dict[ind][nd] == w
            assert nd not in input_dict[ind]
    for nd, indict in output_dict.items():
        for ind, w in indict.items():
            assert input_dict[ind][nd] == w
            assert nd not in output_dict[ind]

    # bucket keys are either 'sinks', 'sources' or the weight of outputs - weight of inputs (as in the algo described
    # in the paper, just weighted).
    def compute_bucket_key_for_node(n):
        if not output_dict.get(n):
            return 'sinks'
        elif not input_dict.get(n):
            return 'sources'
        else:
            return sum(output_dict[n].values()) - sum(input_dict[n].values())

    buckets = {}

    def insert_to_bucket(node, key):
        if key in buckets:
            buckets[key].add(node)
        else:
            buckets[key] = {node}

    def remove_from_bucket(node, key):
        buckets[key].remove(node)
        if not buckets[key]:
            del buckets[key]

    def pop_from_bucket(key):
        itm = buckets[key].pop()
        if not buckets[key]:
            del buckets[key]
        return itm

    # insert all nodes to buckets
    for n in range(len(nodes_compacted)):
        insert_to_bucket(node=n, key=compute_bucket_key_for_node(n))

    # keep index of node to bucket key for quick lookups
    node_to_bucket_key = {}
    for k, v in buckets.items():
        for n in v:
            node_to_bucket_key[n] = k

    if verbosity > 1:
        blengths = {k: len(v) for k, v in buckets.items()}
        print(f"Buckets: {blengths}")

    # see the algo description in sec 2.3 - left contains sources and "lots of outputs" nodes, right contains sinks
    left_list = []
    right_list = []

    # for periodic progress report
    next_report = 1
    num_nodes = len(nodes_compacted)

    # this will update all data structures upon removal of a node and it's assignment to one of left/right lists
    def update_removed_node(node):
        # update graph
        in_nodes = input_dict[node]
        for inode in in_nodes.keys():
            del output_dict[inode][node]

        out_nodes = output_dict[node]
        for onode in out_nodes.keys():
            del input_dict[onode][node]

        del input_dict[node]
        del output_dict[node]

        # update buckets and bucket key index
        for ionode in set(in_nodes.keys()).union(out_nodes.keys()):
            remove_from_bucket(node=ionode, key=node_to_bucket_key[ionode])
            new_key = compute_bucket_key_for_node(ionode)
            node_to_bucket_key[ionode] = new_key
            insert_to_bucket(node=ionode, key=new_key)

    # main algo execution loop. repeats untill all nodes have been assigned to left or right list
    while len(left_list) + len(right_list) < num_nodes:
        # check in for progress report
        if verbosity > 1 and len(left_list) + len(right_list) > next_report:
            pld = len(left_list) + len(right_list)
            print(f"Placed {pld} nodes ({percentage_str(pld, num_nodes)}) fs = {feedback_val} ({percentage_str(feedback_val, total_edge_weight)})")
            next_report += 1000

        # if there are sinks in remaining subgraph, assign them to the right list
        if buckets.get('sinks'):
            n = pop_from_bucket('sinks')
            right_list.append(n)
            if verbosity > 1:
                print(f"Removing {n} coz sink")
            update_removed_node(n)
        # if there are sources in remaining subgraph, assign them to the left list
        elif buckets.get('sources'):
            n = pop_from_bucket('sources')
            left_list.append(n)
            if verbosity > 1:
                print(f"Removing {n} coz source")
            update_removed_node(n)
        # if no sinks/sources in remaining subgraph, assign the node with largest out/in weight ration to the left list
        else:
            max_delta = max(buckets.keys() - {'sinks', 'sources'})
            n = pop_from_bucket(max_delta)
            left_list.append(n)
            max_delta_feedback = sum(input_dict[n].values())
            feedback_val += max_delta_feedback
            if verbosity > 1:
                print(f"Removing {n} coz delta {max_delta} (fs inc {max_delta_feedback})")
            update_removed_node(n)

    # right list needs to be reversed since sinks were appended to the end instead of beginning (impl detail)
    right_list.reverse()

    # go back from compacted ids to original node ids
    nodes_compacted_inverse = {k: v for v, k in nodes_compacted.items()}
    res = [nodes_compacted_inverse[n] for n in left_list + right_list]
    res_index = {v: k for k, v in enumerate(res)}

    # sanity check: the total weight of edges going backward in the computed order should equal the tracked weight
    fs = [r for r in connections if res_index[r[0]] > res_index[r[1]]]
    fsw = sum([r[2] for r in fs])
    assert fsw == feedback_val

    if verbosity > 0:
        print(f"Feedback size: {fsw}:{feedback_val} ({percentage_str(fsw, total_edge_weight)})")
        if verbosity > 1:
            print(f"Result: {res}")
            print(f"Feedback: {fs}")

    return res

connectome_df = pd.read_csv('../connectome.csv')
connections = list(connectome_df.itertuples(index=False, name=None))

# Compute the order
order = compute_order(connections)

# Calculate the score (sum of weights of edges pointing forward)
node_to_order = {node: idx for idx, node in enumerate(order)}
forward_sum = sum(weight for source, target, weight in connections
                  if node_to_order[source] < node_to_order[target])

# Prepare the output CSV
output_df = pd.DataFrame({'Node ID': order, 'Order': range(len(order))})
output_df.to_csv('demo_submission.csv', index=False)

print(f"Score (sum of forward edge weights): {forward_sum}")
print("Output saved to submission_order.csv")

the code above is a benchmark, the default code. the code solution that you come up with must be better than this. Currently, the benchmark is 69% and 29,023,882 sum of weights. Create a solution better than this. The solution doesn't have to follow the same style or method as the benchmark.