Id |
Subject |
Object |
Predicate |
Lexical cue |
T1 |
52-190 |
DRI_Background |
denotes |
Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. |
T2 |
191-351 |
DRI_Challenge |
denotes |
The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. |
T3 |
352-473 |
DRI_Background |
denotes |
However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. |
T4 |
474-643 |
DRI_Background |
denotes |
In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. |
T5 |
644-800 |
DRI_Background |
denotes |
Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. |
T6 |
801-931 |
DRI_Challenge |
denotes |
This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. |
T7 |
932-1067 |
DRI_Approach |
denotes |
Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. |
T8 |
1068-1179 |
DRI_Approach |
denotes |
We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. |
T9 |
1180-1381 |
DRI_Outcome |
denotes |
Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure. |