Exact stochastic constraint optimisation with applications in network analysis

In business, governance, science as well as in our daily lives, we often have to solve problems that involve optimal decision-making under constraints (limitations on the choices that we can make) and uncertainty (probabilistic knowledge about the future).

There are many stochastic constraint (optimisation) problems (SCPs) that arise from probabilistic networks. These are networks where we associate a probability with the nodes and/or edges of the network.

Going viral

One of those problems is the spread of influence (or viral marketing) problem. We are given a social network with probabilistic influence relationships. Suppose Alexa has a probability of 0.8 to convince Claire to do something. We can model this with a node that symbolises Alexa and a node that symbolises Claire, and an edge pointing from Alexa to Claire, labelled with probability 0.8.

In this network, we assume all influence relationships to be symmetric. Hence, there is an edge with label 0.8 pointing from node a (Alexa) to node c (Claire), and the same arrow pointing back, resulting in one undirected edge connecting these two nodes.

Suppose now that we are a company planning a marketing campaign to promote our product. We want to use a word-of-mouth strategy on the probabilistic social network that we have of our potential customers. The way to start the process is to give free samples of our product to certain people in the network, and hope that they like it and convince their friends to buy the product, who then convince their friends, and so on, until our product goes viral. We have a limited budget for this (the constraint), and want to maximise the expected number of people who will eventually buy our product (the optimisation criterion). Which people in the network should get a free sample of our product?

Alternatively, we can require that the expected number of people who become customers exceeds a certain threshold (the constraint), while we aim to minimise the number of free samples that are distributed (the optimisation criterion). In practice, we can solve a stochastic constraint optimisation problem with a stochastic optimisation criterion and a budget constraint by formulating it as a stochastic constraint satisfaction problem (CSP) with a stochastic constraint and budget constraint. We then start the solving process with a very low threshold for the stochastic constraint (e.g., 0), and increase it each time we find a solution to the CSP. Then, we continue the search for a better solution until no better solution can be found. By then, the last solution we have found is an optimum solution.

Finding an optimum solution is hard

It is the stochastic constraint mentioned above that makes it hard to find a solution to SCPs. Given a strategy (a choice for which people receive a free sample), computing the probability that a person will become one of our customers is expensive. It involves summing probabilities over many, often partially overlapping, paths in the network. We then have to do this for all people in the network (to get the expected number of customers), and potentially have to repeat this for all possible strategies, in order to find a strategy that maximises the expected number of customers (by repeatedly finding an expected number of customers that exceeds an ever-increasing threshold of the stochastic constraint).

Joining forces

To find a solution to the problem, we therefore need two things: a way to quickly reason about probabilities, and a way to traverse the search space of strategies intelligently. We can combine the technique of knowledge compilation with the paradigm of constraint programming to create data structures (specifically, decision diagrams) that capture information about probability distributions, such that we can quickly compute probabilities, and to create constraint propagation algorithms on those data structures, which prune the search space.

Dedicated constraint propagation yields faster solving

A constraint propagation algorithm helps to prune the search space by removing values from the domains of variables. In particular, it ensures that variable domains contain no values that would falsify the constraint(s) in which the variables are present. Comparing different approaches that combine knowledge compilation and constraint programming, we find that having a dedicated constraint propagation algorithm for stochastic constraints tends to yield shorter solving times than approaches that decompose stochastic constraints into small, linear constraints (for which propagators already exist). We attribute this to the fact that in the decomposition process, we lose information about the relationship between different variables that participate in the stochastic constraint. Hence, the pruning power of the propagation methods is less, and the solver cannot prune certain parts of the search space that do not contain any solutions.


You can learn more about different ways of combining knowledge compilation and constraint programming in our paper “Exact stochastic constraint optimisation with applications in network analysis”, which was published in Artificial Intelligence, vol 304, March 2022.
Authors: Anna L.D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos, Siegfried Nijssen.
Link: doi.org/10.1016/j.artint.2021.103650