The long-term impact of ranking algorithms in growing networks

Abstract

When we search online for content, we are constantly exposed to rankings. For example, web search results are presented as a ranking, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google's PageRank) have been extensively studied in previous works, we still lack a clear understanding of their potential systemic consequences. In this work, we fill this gap by introducing a new model of network growth that allows us to compare the properties of the networks generated under the influence of different ranking algorithms. We show that by correcting for the omnipresent age bias of popularity-based ranking algorithms, the resulting networks exhibit a significantly larger agreement between the nodes' inherent quality and their long-term popularity, and a less concentrated popularity distribution. To further promote popularity diversity, we introduce and validate a perturbation of the original rankings where a small number of randomly-selected nodes are promoted to the top of the ranking. Our findings move the first steps toward a model-based understanding of the long-term impact of popularity-based ranking algorithms, and could be used as an informative tool for the design of improved information filtering tools.

Network-based ranking in social systems: three challenges

“…Can we predict how the adoption of an algorithm in a given system will alter the agents' behavior and further influence the evolution of the system? Most studies that aimed to answer this question have focused on agent-based models and network formation models [57,[59][60][61][62]. In the following, we highlight three examples of insights that can be gained from stochastic models of network formation.…”

“…In the following, we highlight three examples of insights that can be gained from stochastic models of network formation. [57]. Control parameter β determines the relative importance of ranking and fitness, whereas parameter α determines how sensitive the agents are to the other nodes' ranking position.…”

“…Surprisingly, when agents are highly sensitive to ranking (α > 1, roughly), all popularity-based metrics lead to network with a lower popularity-quality correlation than networks where agents follow a random ranking (black lines). Adapted from [57].…”

“…Second, in a growing network, if the new nodes choose their connections driven by a ranking metric that is biased by node age, the resulting system will exhibit uneven popularity distribution, and the overall correlation between talent and success will be low [57]. This property can be observed in a growing network model where, when establishing new links, a node is (with probability β) driven by the ranking position of the preexisting nodes, or (with probability 1 − β) by their intrinsic fitness-a parameter that quantifies the inherent appeal of the node.…”

Identification of influencers in complex networks by local information dimensionality

“…In addition to new measures such as the H-index centrality [44], those in optimal percolation theory [3] and evidence theory [15], the technique for order preference by similarity to the ideal solution (TOPSIS) [48], and other measures [47,31,46]. These centrality measures have been applied in various fields such as game theory [32], human cooperation [19], evolutionary games [20], relevant website ranking [49], and node synchronization [4,38]. However, these classical centrality measures have limitations.…”