Weighted network motifs as random walk patterns

Abstract Over the last two decades, network theory has shown to be a fruitful paradigm in understanding organization and functioning of real-world complex systems. One technique helpful this endeavor is identifying functionally influential subgraphs, shedding light on underlying evolutionary processes. Such overrepresented motifs , have received much attention simple networks, where edges are either or off. However, for weighted motif analysis still undeveloped. Here, we proposed novel methodology—based random walker taking fixed maximum number steps—to study limited size. We introduce sink node balance allow detection configurations within an priori steps walker. applied approach different real networks selected specific null model based maximum-entropy test significance occurrence. found that identified similarities enable classifications systems according mechanisms associated with configurations: economic exhibit close patterns while differentiating from ecological without any assumption.