Big Data is defined as an emerging paradigm that includes complex and large-scale information beyond the processing capability of conventional tools. Traditional data analytics methods have been commonly used for many applications, such as text classfication, image recognition, and video tracking. For analysis purposes, these data often need to be represented as vectors. However, many other types of data objects in real-world applications contain rich feature vectors and structure information, such as chemical com pounds in bio-pharmacy, brain regions in brain networks and users in social networks. Unfortunately, vector representa tions are very simple features that do not inherently contain the object’s structure information. In reality, objectsmay have complicated characteristics depending on how the objects are assessed and characterized. Data may also reside in het erogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, and semi-structured data. As a result, novel data analytics methods are needed to discover meaningful knowledge in advanced applications from objects with large-scale com plex characteristics. In total, there were 104 submissions to this IEEE ACCESS Special Section, with 39 accepted after rigorous peer review.