package graph_mining

Configuration proto for ConvertToUndirected. Next available id: 5

optional ConvertToUndirectedConfig.AsymmetricEdgeTreatment asymmetric_edge_treatment = 1
oneof edge_set_transformation
Specifies how to transform the edge set into an undirected graph. Setting no option is equivalent to setting remove_one_sided_edges to false.
- bool remove_one_sided_edges = 2
  If set to false, the graph is made undirected by adding reciprocal edges. Otherwise, edges with no reciprocal edges are removed. In *both* cases the output contains a Node for each endpoint of each edge in the input. In particular, we may add a Node that ends up being isolated in the output.
- ConvertToUndirectedConfig.Sparsify sparsify = 3
  See the comments for Sparsify message above.
optional string counter_prefix = 4
Prefix for Flume counters.

Specifies how to compute the edge weight of an edge x -> y, given the weights of edges x -> y and y -> x. Most of the options are self-descriptive.

MAX = 0
MIN = 1
REMOVE = 2
If the weights differ by more than 1e-6, remove the edges. Otherwise use one of the two weights.
AVG = 3
SUM = 4

optional int32 soft_degree_threshold = 1
Let s = soft_degree_threshold and h = hard_degree_threshold. The following steps are performed. 1. If s is set, for each node we discard all but s edges with highest weight (using node id hashes for tiebreaking). 2. We convert the graph to an undirected one, by adding reciprocal edges. 3. If h is set, we repeat (1) using h as a threshold and then make the graph undirected by removing one sided edges. At least one of *_degree_threshold has to be set.
optional int32 hard_degree_threshold = 2
optional bool keep_lowest_weight_edges = 3
By default the highest weight edges are keep unless keep_lowest_weight_edges is set to true.

Config to employ dynamically changing weight threshold in each round of affinity clustering. Next available id: 4

optional DynamicWeightThresholdConfig.WeightDecayFunction weight_decay_function = 1
Specifies how the weight threshold changes each round. NOTE: originally this only supported decreasing edge weights in each step (hence the 'decay' in the name, and the threshold in the first step is 'upper_bound'). However, *increasing* weights in each round is also supported and can be achieved by setting lower_bound > upper_bound.
optional double upper_bound = 2
Upper bound is the weight threshold that will be used in the first round.
optional double lower_bound = 3
Lower bound is the weight threshold that will be used in the last round.

UNKNOWN_FUNCTION = 0
LINEAR_DECAY = 1
Changes the weight threshold linearly each iteration.
EXPONENTIAL_DECAY = 2
Changes the weight threshold exponentially each iteration. Each iteration, a new weight threshold is computed by the following formula: upper_bound*((lower_bound/upper_bound)^(round/compression_rounds))

message ConvertToUndirectedConfig