  Aggregate network properties such as cluster cohesion and the number of
bridge nodes can be used to glean insights about a network's community
structure, spread of influence and the resilience of the network to faults.
Efficiently computing network properties when the network is fully observed has
received significant attention (Wasserman and Faust 1994; Cook and Holder
2006), however the problem of computing aggregate network properties when there
is missing data attributes has received little attention. Computing these
properties for networks with missing attributes involves performing inference
over the network. Statistical relational learning (SRL) and graph neural
networks (GNNs) are two classes of machine learning approaches well suited for
inferring missing attributes in a graph. In this paper, we study the
effectiveness of these approaches in estimating aggregate properties on
networks with missing attributes. We compare two SRL approaches and three GNNs.
For these approaches we estimate these properties using point estimates such as
MAP and mean. For SRL-based approaches that can infer a joint distribution over
the missing attributes, we also estimate these properties as an expectation
over the distribution. To compute the expectation tractably for probabilistic
soft logic, one of the SRL approaches that we study, we introduce a novel
sampling framework. In the experimental evaluation, using three benchmark
datasets, we show that SRL-based approaches tend to outperform GNN-based
approaches both in computing aggregate properties and predictive accuracy.
Specifically, we show that estimating the aggregate properties as an
expectation over the joint distribution outperforms point estimates.
