4 → 1 cross-prediction accuracy.

<p>Minimal (lower triangle) and maximal (upper triangle) prediction accuracy for all five member recruitment models are shown as a function of platform penetration and the disclosure parameter . Upper row: ; lower row: ; black triangles denote data points where was smaller than the according fraction of positive samples among all samples.</p

Membership propagation in a toy example according to different propagation models.

<p>Note that real social networks exhibit more long-range edges. Examples for the platform penetration value show the nodes from which the propagation started (black nodes with white core). Other members are marked black and relevant non-members red; for ease of reading arrows are not displayed, but black edges are bidirectional while green edges point from black to red nodes. With BFS and DFS the network is explored starting from one node (denoted by a white circle); with RW and EN there are more nodes from which the propagation is launched; and finally, for RS all selected nodes can be seen as starting nodes.</p

Comparison of basic network analytic statistics of the five data sets obtained from Traud et al. [<b>22</b>].

<p>Comparison of basic network analytic statistics of the five data sets obtained from Traud et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034740#pone.0034740-Traud1" target="_blank">[<b>22</b>]</a>.</p

Definitions and examples.

<p>Any social network platform divides society into two sets: the set of members (black nodes) and of non-members . In our toy example of individuals, i.e. a fraction of , are members. The relevant subset of non-members (red nodes) that are in contact with at least one member is distinguished from other non-members (gray nodes). of the members, i.e., a fraction of , have disclosed their outside social contacts. The knowledge of the set of edges between members (black, bi-directed) and the set of edges (green) to non-members is enough to infer a substantial fraction of edges between non-members (red edges).</p

Features based on different edge sets between the exclusive, joint, and common neighborhoods of <i>v</i> and <i>w</i>.

<p>All left-hand nodes belong to the joint neighborhood of and . is exclusive to , while are exclusive to , and are common neighbors of both. Our features comprise the absolute number of edges between common neighbors (black, dashed edges), exclusive neighbors (black, straight edge), joint neighborhood (all black edges between nodes ), and an exclusive and a common neighbor (black, dotted edges). For each of them we also added their normalized value. Normalization was done by the number of possible edges between the neighbors they have.</p

Synapses, detected in the test dataset.

<p>A: A 3D view of the synapses, detected in the test dataset. The detected synapses are shown in green. The central slice of the raw data is also shown for illustration (the shape distortion is caused by elastic stack registration). The data volume was downsampled by a factor of 10 in the x and y dimensions to show the full volume. B, C, D, E: More detailed synapse examples as an image series. Scale bars: 450 nm, every second slice is shown (distance between consecutive images is 90 nm).</p

False negative errors.

<p>A - distribution of false negative errors as a function of the synapse average cross section area and continuity in z. For each bin of the 2D histogram its count is proportional to the radius of the displayed circle. Cross section area was measured in pixels and averaged across 5 central slices. B, C - serial sections of false negative detections. D – erroneous algorithm segmentation. All scale bars – 450 nm.</p

Object features.

<p>Features, used for object classification. The size of the neighborhood is specified in pixels for x, y and z dimensions. The neighborhood was computed using anisotropic distance transform in 3D.</p

False negative errors against synapse size and perforation.

<p>Left: distribution of false negative errors as a function of synapse size (see text for details on size estimation). Right: distribution of false negative detections as a function of the number of slices, where the synapse is perforated.</p

The proposed synapse detection pipeline.

<p>Left to right: raw data with 3 synapses, shown in green circles; probability map of the synapse class; detection results; graph cut segmentation results; object classification results, with positively classified objects shown in green and the negatively classified object in red.</p