# Deep Link Entropy for Quantifying Edge Significance in Social Networks
## 4 Discussion  
An important and interesting point in our approach is determining the coefficient of the adjacent nodes, \(\chi\). We anticipate possibly a different and even unique \(\chi\) for each network for achieving the highest performance. Hence, we state an interesting open problem for future research to develop a theory for extracting the optimum value of \(\chi\) directly from the network structure. Without extracting the value in a theoretical basis, however, it is possible to use optimization techniques to determine \(\chi\). Nevertheless, we were able to find \(\chi\) values easily such that DLE can outperform LE. On the first hand, we obtained our results for all the networks for a single value, i.e., \(\chi=-0.12\). On the other hand, failing to find a non-zero \(\chi\) for a given network, setting \(\chi=0\), the present DLE approach reduces to LE. Because the main purpose of the present work is to improve the LE approach by taking the adjacent nodes into account, we leave the open questions regarding \(\chi\), together with other benchmarking networks such as dolphins or polblogs networks for future research. Note that the results we found herein regarding the LE approach might look slightly different than the results of the original LE work, due to the scaling of the figures and to the update mechanism we employ. A crucial result of the present work is that according to our strategy, more than a continuous and smooth decrease, \(R_{GC}\) exhibits sudden drops. In the former case, one node or a few nodes are continuously disintegrated from the overall network. However in the latter case, following the removal of edges between groups (with a nearly constant \(R_{GC}\)), as the last connecting edge is removed, one group completely disintegrates from the rest of the network, which we probe by a sudden drop of \(R_{GC}\). Both cases have slightly different implications from the perspectives of individuals or groups disintegrating from the society, respectively. Regarding political communications in online social networks in particular, through removing the edge quantified as the most significant in each step, our proposal disintegrates the groups from each other and from the society. This means that our approach successfully detects the edges constituting online bridges between groups which are fragmented along ideological lines. Beyond the problem of analyzing whether online political communications reinforce or weaken the echo chambers among fragmented groups and the group polarization [10; 11; 12; 13; 14; 15], achieving a high performance edge significance quantification, we believe our work contributes to the field by putting forward an operational framework. That is, isolation through disintegrating from other different-minded groups and eventually from the society, echo chambers are formed and cemented within ideological groups, posing a threat to democracy. Hence, in order to strengthen the interaction between groups, detecting edges bridging the groups paves the way for social engineering towards an advanced democracy. Social network sites (SNS) are already implementing advanced algorithms for detecting accounts to suggest others to connect, i.e., to _follow_ or to _become friends_. Their algorithms are plausibly targeting to increase the network traffic for maximizing their profit. For social engineering towards an advanced democracy--possibly through regulations--SNS could consider updating their algorithms to consider the global connectivity and diffusion of ideologies among communities. We conceptualize two sets of actions, (i) to keep existing connections, and (ii) to create new connections. For the first set, considering the fact that nodes are disconnecting from others (through unfollowing, or unfriending) time to time due to a decrease in interactions, actions such as bringing the posts of one particular node to the timeline of another particular node more frequently can help increasing the interactions, hence, keeping the existing connections.