3 research outputs found
Gaps in Information Access in Social Networks
The study of influence maximization in social networks has largely ignored
disparate effects these algorithms might have on the individuals contained in
the social network. Individuals may place a high value on receiving
information, e.g. job openings or advertisements for loans. While
well-connected individuals at the center of the network are likely to receive
the information that is being distributed through the network, poorly connected
individuals are systematically less likely to receive the information,
producing a gap in access to the information between individuals. In this work,
we study how best to spread information in a social network while minimizing
this access gap. We propose to use the maximin social welfare function as an
objective function, where we maximize the minimum probability of receiving the
information under an intervention. We prove that in this setting this welfare
function constrains the access gap whereas maximizing the expected number of
nodes reached does not. We also investigate the difficulties of using the
maximin, and present hardness results and analysis for standard greedy
strategies. Finally, we investigate practical ways of optimizing for the
maximin, and give empirical evidence that a simple greedy-based strategy works
well in practice.Comment: Accepted at The Web Conference 201
Reducing Access Disparities in Networks using Edge Augmentation
In social networks, a node's position is a form of \it{social capital}.
Better-positioned members not only benefit from (faster) access to diverse
information, but innately have more potential influence on information spread.
Structural biases often arise from network formation, and can lead to
significant disparities in information access based on position. Further,
processes such as link recommendation can exacerbate this inequality by relying
on network structure to augment connectivity.
We argue that one can understand and quantify this social capital through the
lens of information flow in the network. We consider the setting where all
nodes may be sources of distinct information, and a node's (dis)advantage deems
its ability to access all information available on the network. We introduce
three new measures of advantage (broadcast, influence, and control), which are
quantified in terms of position in the network using \it{access signatures} --
vectors that represent a node's ability to share information. We then consider
the problem of improving equity by making interventions to increase the access
of the least-advantaged nodes. We argue that edge augmentation is most
appropriate for mitigating bias in the network structure, and frame a budgeted
intervention problem for maximizing minimum pairwise access.
Finally, we propose heuristic strategies for selecting edge augmentations and
empirically evaluate their performance on a corpus of real-world social
networks. We demonstrate that a small number of interventions significantly
increase the broadcast measure of access for the least-advantaged nodes (over 5
times more than random), and also improve the minimum influence. Additional
analysis shows that these interventions can also dramatically shrink the gap in
advantage between nodes (over \%82) and reduce disparities between their access
signatures
Information access representations and social capital in networks
Social network position confers power and social capital. In the setting of
online social networks that have massive reach, creating mathematical
representations of social capital is an important step towards understanding
how network position can differentially confer advantage to different groups
and how network position can itself be a source of advantage. In this paper, we
use well established models for information flow on networks as a base to
propose a formal descriptor of the network position of a node as represented by
its information access. Combining these descriptors allows a full
representation of social capital across the network. Using real-world networks,
we demonstrate that this representation allows the identification of
differences between groups based on network specific measures of inequality of
access