  The recent emergence of the promising paradigm of the Social Internet of
Things (SIoT) is a result of an intelligent amalgamation of the social
networking concepts with the Internet of Things (IoT) objects (also referred to
as "things") in an attempt to unravel the challenges of network discovery,
navigability, and service composition. This is realized by facilitating the IoT
objects to socialize with one another, i.e., similar to the social interactions
amongst the human beings. A fundamental issue that mandates careful attention
is to thus establish, and over time, maintain trustworthy relationships amongst
these IoT objects. Therefore, a trust framework for SIoT must include
object-object interactions, the aspects of social relationships, credible
recommendations, etc., however, the existing literature has only focused on
some aspects of trust by primarily relying on the conventional approaches that
govern linear relationships between input and output. In this paper, an
artificial neural network-based trust framework, Trust-SIoT, has been envisaged
for identifying the complex non-linear relationships between input and output
in a bid to classify the trustworthy objects. Moreover, Trust-SIoT has been
designed for capturing a number of key trust metrics as input, i.e., direct
trust by integrating both current and past interactions, reliability, and
benevolence of an object, credible recommendations, and the degree of
relationship by employing a knowledge graph embedding. Finally, we have
performed extensive experiments to evaluate the performance of Trust-SIoT
vis-a-vis state-of-the-art heuristics on two real-world datasets. The results
demonstrate that Trust-SIoT achieves a higher F1 and lower MAE and MSE scores.
