Complex networks can be understood as an integration of graph theory and statistical physics. Because of their flexibility and generality, complex networks provide a primary choice for representing, characterizing and modeling most natural phenomena. In other words, complex networks are just great for scientific modeling.
Thus, complex networks have allowed the effective integration and enhancement of my previous researches in image analysis/vision and pattern recognition, two areas which are themselves closely interrelated.
Indeed, in addition to using complex networks concepts and methods in these two fields, interesting perspectives have also been defined by applying concepts from those areas to complex networks.
More info at our dedicated page: Complex Networks