The 7 Graphs of Algorithms: You’re Not Unknown

Let’s explore the dif­fer­ent graphs of algorithms.

To the unini­ti­ated, an algorithm might seem as daunt­ing as deci­pher­ing hiero­glyphs or nav­ig­at­ing the shift­ing paths of a quantum universe. 

Yet, at their core, algorithms are just a series of graphs, eleg­ant recipes of the com­pu­ta­tion­al world, mar­ry­ing logic and cre­ativ­ity in a math­em­at­ic­al dance, orches­trat­ing our world’s tech­no­lo­gies from the shadows.

What are these graphs — and what can they do? 

Here we go:

The Secret Architects of the Digital Epoch

In the gentle hum of the 21^st-cen­tury land­scape, in the sil­hou­ettes of the tall serv­er racks and the twink­ling lights of the ever-ubi­quit­ous smart­phones, one finds the quiet pulse of a new glob­al life force: the algorithm. 

Unseen yet omni­present, algorithms are the secret archi­tects of our digit­al lives, under­ground labyrinths of cable and invis­ible tsuna­mis of radi­owaves, extend­ing tendrils into every corner of our existence.

An algorithm could be as simple as sort­ing a list of num­bers or as com­plex as a multi-layered deep neur­al net­work trained to recog­nize human faces in a bust­ling crowd.

As we nav­ig­ate the com­plex­ity of this digit­al epoch, it’s our respons­ib­il­ity to ensure that the vast poten­tial of these algorithmic leviath­ans is har­nessed to illu­min­ate the human exper­i­ence, not to shroud it. 

The Paradoxical Power of Algorithms

In today’s world, algorithms pro­pel us into the digit­al age. They fil­ter the news we read, optim­ize our travel routes, sug­gest the music we listen to, and even find romantic partners. 

Algorithms are the pup­pet­eers behind the grand spec­tacle of online advert­ising, decid­ing in split seconds which ads to show us based on an intric­ate tapestry of data points. The world of fin­ance, too, has bowed to their reign, with algorithmic trad­ing becom­ing the pulsat­ing heart­beat of Wall Street.

The power algorithms hold is immense and, quite pos­sibly, bey­ond our com­pre­hen­sion. They’re no longer just tools for execut­ing mundane tasks; they’ve evolved to learn, pre­dict, and, in a sense, under­stand. In light of rap­id devel­op­ments in AI, who knows what the mul­ti­pli­er will be?

They offer a mir­ror to our col­lect­ive psyche, cap­tur­ing pat­terns in our beha­viour, our desires, and our fears. Yet, therein lies the para­dox. This power — beau­ti­ful and ter­ri­fy­ing — can be wiel­ded for the great­er good or serve as a con­duit for manip­u­la­tion, rein­for­cing biases, and infringing on privacy.

Who knows?

Shaping Norms and Sculpting Futures

The might of today’s algorithms is their abil­ity to sort, recom­mend, fil­ter, and per­son­al­ise and their capa­city to sway human beha­viour and subtly sculpt soci­et­al norms. The pre­cise meas­ure of their influ­ence is still being unravelled. 

Yet, what is bey­ond doubt is that our lives have become entwined with these math­em­at­ic­al minotaurs. We live in a world painted with broad strokes of human cre­ativ­ity but filled in with the del­ic­ate brush­work of algorithms.

In the grand scheme of human exist­ence, the algorith­m’s reign is rel­at­ively nas­cent, and our soci­ety is still grap­pling with the implic­a­tions of its influ­ence. As we look towards a future where algorithms will undeni­ably play a cent­ral role, it becomes more cru­cial than ever to under­stand their power and to ensure their use is trans­par­ent, fair, and bene­fi­cial for all. 

Their eth­er­e­al whis­pers, rever­ber­at­ing through the cir­cuits of the digit­al cos­mos, must be guided by the prin­ciples of human­ity as they con­tin­ue to write the story of our era.

The Architecture of Graphs and Networks

Amid the flux of bytes and pixels, our col­lect­ive con­scious­ness is emer­ging, shap­ing and shap­ing a new archi­tec­ture. It is an archi­tec­ture of graphs and net­works, map­ping our desires and curi­os­it­ies, fears and friend­ships, com­merce and culture.

Akin to the star maps that once guided explorers through vast, uncharted seas, these graphs chart the vast and ever-expand­ing seas of data, serving as celes­ti­al com­passes for the titans of the digit­al realm — the search engines, the social net­works, the behemoth online ser­vices that boast user bases as pop­u­lous as nations. 

Each click, each like, each share and tweet weaves into an intric­ate tapestry of user data, provid­ing these digit­al giants with a wealth of insights to max­im­ise their long-term busi­ness poten­tial, sim­ul­tan­eously shap­ing our digit­al inter­ac­tions and reflect­ing our col­lect­ive desires. 

When viewed through the lens of the sev­en types of graphs, this data forms the skel­et­on key to our digit­al identities.

Enter: The 7 Graphs of Algorithms

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Types of Algorithm Graphs

Search engines, social net­works, and online ser­vices typ­ic­ally have a wealth of user data to optim­ise the user experience.

Here are examples of dif­fer­ent types of graphs that social media algorithms use to shape desired behaviours:

• Social graph. The media com­pany can access your friend list and push their con­tent (or favoured con­tent) into your feed.
• Interest graph. The media com­pany can access your interests (top­ics, per­sons of interest, dif­fi­culty level, format pref­er­ences, on-plat­form-spe­cif­ic beha­viours, etc.) from your usage history.
• Predictive graph. The media com­pany can access all graphs from users not con­nec­ted to you but with whom you share a stat­ist­ic­al like­ness and show their pre­ferred con­tent to you.
• Prescriptive graph. The media com­pany can push con­tent into your feed to manip­u­late your over­all emo­tion­al exper­i­ence when using the platform.
• Trend graph. The media com­pany can push con­tent into your feed based on what seems to be trend­ing on the platform.
• Contextual graph. The media com­pany can access con­tex­tu­al data like loc­a­tion, weath­er, cal­en­dar events, affil­i­ations, world events, and loc­al events.
• Commercial graph. The media com­pany can access data on how you and oth­ers like you inter­act with com­mer­cial content.

The dif­fer­ent graphs are typ­ic­ally weighted dif­fer­ently. For instance, some media com­pan­ies allow a fair degree of social graph con­tent, while oth­ers offer almost none. Changes are con­stantly being enforced, and the silent switch might be the most not­able example of a media com­pany shift­ing away from the social graph. ¹Silfwer, J. (2021, December 7). The Silent Switch — A Stealthy Death for the Social Graph. Doctor Spin | The PR Blog. https://​doc​tor​spin​.net/​s​i​l​e​n​t​-​s​w​i​t​ch/

The media com­pany can lever­age these graphs using two main approaches:

• Matching. The media com­pany can use vari­ous graphs to gen­er­ate your social feed. Depending on the com­plex­ity of the ana­lys­is, this approach is slow and expens­ive with react­ive (unpre­dict­able) results.
• Profiling. The media com­pany can use vari­ous graphs to place you in stat­ist­ic­al sub­groups, allow­ing con­tent to iter­ate to the right audi­ence. This approach is fast and cheap with pro­act­ive (pre­dict­able) results.

Today, pro­fil­ing seems to be the dom­in­ant approach amongst media companies.

Learn more: The 7 Graphs of Algorithms: You’re Not Unknown

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PR Resource: The Silent Switch

The Silent Switch

All social media algorithms are built dif­fer­ently and are con­stantly being developed. At the same time, social media users’ beha­viours are evolving.

Still, there was a way that social media algorithms used to behave—and there is a way that social media algorithms behave now.

This has been a fun­da­ment­al but silent switch.

How Social Media Algorithms Used To Behave

For more than a dec­ade, social media algorithms would deliv­er organ­ic reach accord­ing to a dis­tri­bu­tion that looked some­thing like this:

This dis­tri­bu­tion of organ­ic reach enabled organ­isa­tions to use social media des­pite not being “media companies.”

How Social Media Algorithms Behave Today

Today, after the silent shift, social media algorithms deliv­er organ­ic reach more like this:

The increased com­pet­i­tion and soph­ist­ic­a­tion among con­tent cre­at­ors par­tially explain this new type of dis­tri­bu­tion. However, going vir­al is still just as pos­sible for anyone.

How does this work?

The Single Content Algorithm

How can a social net­work pre­dict what users will like? 

Content from a trus­ted cre­at­or trus­ted by a large com­munity of fol­low­ers used to be the lead­ing indic­at­or of future per­form­ance. But today, social net­works have found a bet­ter way to pre­dict con­tent success.

The single con­tent algorithm = when social net­works demote con­tent cre­at­or author­ity to pro­mote single con­tent per­form­ance to max­im­ise user engage­ment for ad revenue.

The single con­tent algorithm presents newly pub­lished con­tent to a lim­ited audi­ence sample size:

If the newly pub­lished con­tent tests suc­cess­fully, the social media algorithm pushes that con­tent to a slightly lar­ger stat­ist­ic­al sub­set. And so on.

This iter­at­ive pro­cess means that single pieces of con­tent worthy of going vir­al will go vir­al, a) even if it takes a longer time, and b) regard­less of the con­tent cre­at­or’s num­ber of followers.

Learn more: The Silent Switch