# Structural Imbalance in a Social Network#

This example solves a structural-imbalance problem, similar to the Leap demo and Jupyter Notebook, to demonstrate using Leap’s hybrid solver service on a problem of arbitrary structure and size.

*Social networks* map relationships between people or organizations onto graphs, with
the people/organizations as nodes and relationships as edges; for example,
Facebook friends form a social network. *Signed social networks* map both friendly and
hostile relationships by assigning to edges either positive or negative values. Such
networks are said to be *structurally balanced* when they can be cleanly divided into
two sets, with each set containing only friends, and all relations between these sets
are hostile. The measure of *structural imbalance* or *frustration* for a signed social
network, when it cannot be cleanly divided, is the minimum number of edges that violate
the social rule, “the enemy of my friend is my enemy.”

Finding a division that minimizes frustration is an NP-hard graph problem (it can be viewed as an expansion of the well-known maximum cut problem).

## Example Requirements#

The code in this example requires that your development environment have Ocean software and be configured to access SAPI, as described in the Initial Set Up section.

## Solution Steps#

Section Workflow Steps: Formulation and Sampling describes the problem-solving workflow as consisting of two main steps: (1) Formulate the problem as an objective function in a supported model and (2) Solve your model with a D-Wave solver.

In this example, a function in Ocean software handles both steps. Our task is mainly to select the sampler used to solve the problem.

## Formulate the Problem#

For a social graph, G, this example simply builds a random sparse graph—using the
NetworkX `random_geometric_graph()`

function, which places uniformly at random a specified number of nodes, problem_node_count,
in a unit cube, joining edges of any two if the distance is below a given radius—and randomly
assigns \(-1, 1\) signs to represent friendly and hostile relationships.

```
>>> import networkx as nx
>>> import random
>>> problem_node_count = 300
>>> G = nx.random_geometric_graph(problem_node_count, radius=0.0005*problem_node_count)
>>> G.add_edges_from([(u, v, {'sign': random.choice((-1, 1))}) for u, v in G.edges])
```

## Solve the Problem by Sampling#

As mentioned above, this example uses Ocean’s dwave_networkx
function, `structural_imbalance()`

, to create the
appropriate BQM to represent
the problem graph and return a solution. It requires just the selection of a sampler.

D-Wave’s quantum cloud service provides cloud-based hybrid solvers you can submit arbitrary BQMs to. These solvers, which implement state-of-the-art classical algorithms together with intelligent allocation of the quantum processing unit (QPU) to parts of the problem where it benefits most, are designed to accommodate even very large problems. Leap’s solvers can relieve you of the burden of any current and future development and optimization of hybrid algorithms that best solve your problem.

Ocean software’s dwave-system
`LeapHybridSampler`

class enables you to easily incorporate
Leap’s hybrid solvers into your application:

```
>>> from dwave.system import LeapHybridSampler
>>> sampler = LeapHybridSampler()
```

Finally, the returned set of frustrated edges and a bicoloring are counted and printed.

```
>>> import dwave_networkx as dnx
>>> imbalance, bicoloring = dnx.structural_imbalance(G, sampler)
>>> set1 = int(sum(list(bicoloring.values())))
>>> print("One set has {} nodes; the other has {} nodes.".format(set1, problem_node_count-set1))
>>> print("The network has {} frustrated relationships.".format(len(list(imbalance.keys()))))
One set has 143 nodes; the other has 157 nodes.
The network has 904 frustrated relationships.
```

The graphic below visualizes the result of one such run.